US20180129994A1

US20180129994A1 - Efficiency enhancements in task management applications

Info

Publication number: US20180129994A1
Application number: US15/450,758
Authority: US
Inventors: Chad Fowler; Benjamen Ljudmilov Mateev
Original assignee: Microsoft Technology Licensing LLC
Current assignee: Microsoft Technology Licensing LLC
Priority date: 2016-11-06
Filing date: 2017-03-06
Publication date: 2018-05-10
Also published as: IL266342B; US20220383235A1; CN109952586A; BR112019007458A2; CN109906463A; RU2019112875A; US11823105B2; CN109937427A; JP2020500354A; CA3042390A1; EP3535715A1; CO2019004600A2; US20180129371A1; CN109906463B; CL2019001127A1; EP3535717A1; WO2018085480A1; BR112019007458A8; US20210103878A1; KR102581622B1

Abstract

Efficiency improvements for electronic task managers and an improved user experience are realized when more relevant and fewer irrelevant tasks are presented to users and users are given greater control in manipulating those task items. By heuristically determining times, locations, and semantics associated with task relevance and integrating the management of tasks into more applications, the functionality of the systems providing for electronic task management is improved, as computer resources are spent with greater utility to the users and the user experience is improved for the users.

Description

CROSS-REFERENCES TO RELATED APPLICATIONS

The present disclosure claims priority to U.S. Provisional Patent Application No. 62/418,268 filed Nov. 6, 2016, the disclosure of which is hereby incorporated by reference in its entirety.

BACKGROUND

Electronic task management systems and applications enable users to track various tasks more efficiently than with hardcopy notes; users can access the same tasks from multiple devices, rearrange the tasks, and share tasks between users remotely. The ease of adding tasks to an electronic task manager, however, can leave users overwhelmed; too many, irrelevant, or contextually inappropriate tasks can distract the user from the tasks that are relevant to the user at a given time and place. The provision of unwanted tasks not only degrades the user experience, but also wastes computing resources that are used to provide tasks that are not wanted by the user that could be used more efficiently for other tasks.

SUMMARY

This summary is provided to introduce a selection of concepts in a simplified form that are further described below in the Detailed Description section. This summary is not intended to identify all key or essential features of the claimed subject matter, nor is it intended as an aid in determining the scope of the claimed subject matter.
Enhancements to the efficiency of a task management application are discussed herein in relation to systems, methods, and computer readable media that provide such enhancements. Relational data for entities and the context in which users interact with task items, including the productivity applications used to complete task items, are used to provide users with more relevant tasks, fewer irrelevant tasks, and with greater control and convenience in manipulating task items.
In one aspect, task items are clustered by their interaction contexts to provide more relevant results to users at different times and locations. Contextual information related to when and where tasks items are added to task lists, marked as complete, viewed, deferred, cancelled, etc., is used in conjunction with semantic data from the entities related to those task items to cluster various task items and task lists. Depending on the time and location at which the user accesses a task management application, a relevant cluster—having an equivalent or similar time and/or location to the user's time and/or location of access—is selected from which task items are to be presented. The user is thus presented with more task items with greater relevance to the time and location at which the task manager is accessed.
By providing enhanced efficiency for a task management application, not only is the user's experience improved, but the functionality of the device used to provide the task management application is also improved. The device spends computing resources (processor cycles and memory storage space) with greater precision; wasting fewer resources to provide unwanted tasks for the user's consideration.
Examples are implemented as a computer process, a computing system, or as an article of manufacture such as a device, computer program product, or computer readable medium. According to an aspect, the computer program product is a computer storage medium readable by a computer system and encoding a computer program comprising instructions for executing a computer process.
The details of one or more aspects are set forth in the accompanying drawings and description below. Other features and advantages will be apparent from a reading of the following detailed description and a review of the associated drawings. It is to be understood that the following detailed description is explanatory only and is not restrictive of the claims.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are incorporated in and constitute a part of this disclosure, illustrate various aspects. In the drawings:

FIG. 1 illustrates an example operating environment in which the present disclosure may be practiced;

FIGS. 2A and 2B are illustrations of example graphical user interfaces in which a task list is implemented;

FIG. 3 illustrates an example graphical user interface in which is displayed suggested tasks on which a user may focus;

FIG. 4A illustrates a calendar application showing one or more events illustrated in relation to a calendar and order of occurrence for a day;

FIG. 4B illustrates a Plan of tasks for the user based on data received from the calendar application illustrated in FIG. 4A;

FIG. 5 illustrates an example graphical user interface in which Task List implementation is provided;

FIG. 6 illustrates a graphical user interface in which a task application provides task list items in a task list category along with additional data about the selected task list item;

FIG. 7 illustrates a graphical user interface in which task list templates are provided to allow users to get specific information about a content item provided as a task item template, such as, for example, a job or a location;

FIG. 8 illustrates various source attributions for suggested tasks items;

FIG. 9 illustrates an example user interface for a digital assistant to create task items;

FIGS. 10A and 10B illustrate example user interfaces for a time period view of a set of task items as the time period's task list is constructed;

FIG. 11 is a flow chart showing general stages involved in an example method for enhancing efficiency by clustering task items by their interaction contexts to provide more relevant results to users at different times and locations;

FIG. 12 is a block diagram illustrating example physical components of a computing device;

FIGS. 13A and 13B are block diagrams of a mobile computing device; and

FIG. 14 is a block diagram of a distributed computing system.

DETAILED DESCRIPTION

The following detailed description refers to the accompanying drawings. Wherever possible, the same reference numbers are used in the drawings and the following description refers to the same or similar elements. While examples may be described, modifications, adaptations, and other implementations are possible. For example, substitutions, additions, or modifications may be made to the elements illustrated in the drawings, and the methods described herein may be modified by substituting, reordering, or adding stages to the disclosed methods. Accordingly, the following detailed description is not limiting, but instead, the proper scope is defined by the appended claims. Examples may take the form of a hardware implementation, or an entirely software implementation, or an implementation combining software and hardware aspects. The following detailed description is, therefore, not to be taken in a limiting sense.
Enhancements to the efficiency of a task management application are discussed herein in relation to systems, methods, and computer readable media that provide such enhancements. Relational data for entities and the context in which users interact with task items, including the productivity applications used to complete task items, are used to provide users with more relevant tasks, fewer irrelevant tasks, and with greater control and convenience in manipulating task items.
In one aspect, task items are clustered by their interaction contexts to provide more relevant results to users at different times and locations. Contextual information related to when and where tasks items are added to task lists, marked as complete, viewed, deferred, cancelled, etc., is used in conjunction with semantic data from the entities related to those task items to cluster various task items and task lists. Depending on the time and location at which the user accesses a task management application, a relevant cluster—having an equivalent or similar time and/or location to the user's time and/or location of access—is selected from which task items are to be presented. The user is thus presented with more task items with greater relevance to the time and location at which the task manager is accessed.
By providing enhanced efficiency for a task management application, not only is the user's experience improved, but the functionality of the device used to provide the task management application is also improved. The device spends computing resources (processor cycles and memory storage space) with greater precision; wasting fewer resources to provide unwanted tasks for the user's consideration.
FIG. 1 illustrates an example operating environment 100 in which the present disclosure may be practiced. As illustrated, a user device 110 is in communication with a task list service 120. In various aspects, the task list service 120 is hosted on the user device 110, while in other aspects, the task list service 120 is hosted on a remote device as a service accessible by the user device 110. The task list service 120 includes in various configurations one or more of a: heuristic engine 121, a suggestion engine 122, a context clusterer 123, a preview generator 124, user profiles 125, a context listener 126, and a relational store 127. The task list service 120 is in further communication with one or more services that may be hosted on the user device 110 or another device that include, but are not limited to: a relational graph service 130, an email service 140, a calendar service 150, and a productivity service 160. Although only one of each component is illustrated in FIG. 1, it will be appreciated that in different aspects more than one of one or more components are provided (e.g., more than one user device 110 is in communication with the task list service 120, which is in communication with more than one email service 140).
Each of the user device 110, task list service 120, and the services 130-160 are illustrative of a multitude of computing systems including, without limitation, desktop computer systems, wired and wireless computing systems, mobile computing systems (e.g., mobile telephones, netbooks, tablet or slate type computers, notebook computers, and laptop computers), hand-held devices, multiprocessor systems, microprocessor-based or programmable consumer electronics, minicomputers, printers, and mainframe computers. The hardware of these computing systems is discussed in greater detail in regard to FIGS. 12, 13A, 13B, and 14. User devices 110 are operated by users, who may be humans or automated systems (e.g., “bots”). In various aspects, the user device 110, task list service 120, and the services 130-160 may be accessed by a user or each other locally and/or by a network, which may include the Internet, a Local Area Network (LAN), a private distributed network for an entity (e.g., a company, a university, a government agency), a wireless ad hoc network, a Virtual Private Network (VPN) or other direct data link (e.g., Bluetooth connection, a direct wired link).
The user device 110 is accessed by a user to operate a task list application, among other features and applications. The task list application provides user-specific tasks that the user wishes to be reminded of to complete and tools for manipulating those tasks (e.g., assign task to another user, share task with another user, complete task, mark status of task, add task, remove task). For example, a user may access the task list application to receive a reminder to pay rent on a given set of days, to attend a meeting at a given time, or to go grocery shopping at an undefined time. In various aspects, the task list application is provided by the task list service 120 in a thin client running on the user device 110 in conjunction with a client running on a remote server. In other aspects, the task list application is provided by a task list service 120 running on the user device 110 as a thick client. In yet other aspects, the task list service 120 operates as a distributed system, running on the user device 110 as a thick client when a network connection to the remote server is not available (or not needed) and as a thin client when the network connection is available.
The task list service 120 includes one or more components that may be enabled or disabled as users enable or disable features or network connections to a remote server are established or lost. In various aspects, a task list service 120 local to a given user device 110 may also disable or reduce in size or complexity one or more components compared to a task list service 120 that is accessible over a network by multiple user devices 110.
A heuristic engine 121 is operable to learn user behavior over time to enhance the determinations of which candidate tasks discovered from task sources are to be presented, and in what order, to a given user at a given time and location. The heuristic engine 121 is operable to use one or more machine learning approaches to determine how to best serve the needs and use-cases presented by individual users.
A suggestion engine 122 is operable to determine whether a candidate task received from a tasks source should be suggested to the user as a task to perform at a given time and/or location. From all of the candidate task items that may be presented to the user at any given time, the suggestion engine 122 filters those task items to a manageable subset based on the user's existing task items (to avoid scheduling conflicts), prior acceptances/rejections of suggested task items, and the prior actions of the user. For example, if a user's calendar includes an event for an upcoming birthday, a suggested task is created that the person whose birthday is coming up should be called prior to that date. In another example, where the user sent an email that included a promise to send an attachment by a deadline, a task is suggested to meet this deadline. In a further example, an important meeting is observed on the calendar service 150 as occurring on Friday, and the suggestion service 122 will observe the rest of the week's calendar to determine which days prior to the meeting are likely to allow for a task item to prepare for the important meeting. For example, the task item will be presented on Monday and Thursday, but not Tuesday or Wednesday, due to the number of task items already accepted for on those days (Tuesday and Wednesday being busier or having more task items accepted in the present example than Monday and Thursday).
A content clusterer 123 is operable to cluster tasks and entities that are related in the location and time of interaction, and the semantics of terms that they contain. As will be understood, clustering is a statistical operation that groups items based on shared characteristics (and combinations thereof). In one aspect, tasks interacted with (created/completed) with similar time ranges are clustered together based on similar time characteristics. In another aspect, tasks interacted with (created/completed) when the user is at a given location will be clustered together based on location characteristics. In a further aspect, tasks with similar words, terms, or entities (persons, documents, resources) will be clustered together based on semantic characteristics. For example, the content clusterer 123 is operable to create two clusters of events when it is noticed that a user performs certain tasks when working at a first location during a first time period and performs other tasks when working at a second location during a second time period to inform the heuristic engine 121 that there are two clusters of activity types regularly performed by the user. The content clusterer 123 enables the suggestion engine 122 to provide suggested tasks that are appropriate for a given time and/or location at which those tasks are presented to the user.
For example, the user will be presented with task items related to work on days associated with the work week and business hours, but will be presented with task items related to domestic activities (e.g., clean bathroom, go shopping, groom dog) outside of business hours. In another example, the user will be presented with tasks related to work when located at the user's place of work (e.g., detected via Global Positioning System (GPS), Internet Protocol (IP) Location Services, network names in range of the user device 110) and domestic tasks when located at another location (e.g., home, the grocery store, the dog groomer). In various aspects, the suggestion engine 122 will place various weights on clustering determinations that may change over a period of time, so that as time progresses, more or less weight will be given to the clustered content's location, time, or semantic data to allow for blended suggestions. For example, as the workday draws to a close, the user may be presented fewer work related tasks for the day as suggestions, and more domestic related tasks (e.g., “pick up milk on the way home from work”). In another example, when a location or a time period unknown to the content clusterer 123 is observed by the suggestion engine 122, the suggestion engine 122 may rely on the other contextual data used to cluster tasks, such as, when a user is on vacation (in a location previously unknown to the suggestion engine 122), the suggestion engine 122 may rely on time context and semantic context to provide suggestions, and ignore locational context.
A preview generator 124 is operable to generate previews for entities associated with a suggested task (or a selected task). For example, a portion of a document that is to be completed as part of a task is extracted by the preview generator 124 for presentation in a user interface as a preview. In another example, a portion of an audio recording of a phone call that is related to a task is generated as a preview. In a further example, a person who is related to a task (as a resource, an assignor, a teammate, or object of the task) has a preview generated with information from the relational graph service 130, such as, for example, that person's contact information, an image of that person, biographical details of that person, etc.
User profiles 125 are stored by the task list service 120 so that as the behaviors of the users are observed by the heuristic engine 121, the observations are stored to provide an increasingly more accurate view of the user's habits and use patterns for predicting future behaviors. In various aspects, the user or an administrator may also manually set preferences in the user profiles 125 to define how tasks are to be presented to the user and aid the heuristic engine 121 in determining the user's preferences in addition to observing the user's actions to learn those preferences.
A context listener 126 is operable to receive (or request) contextual data and task items from the user device 110 and the services 130-160 for use by the task list service 120. In various aspects, these data include appointments, events, meetings, and tasks set for the user and/or accepted by the user in addition to when and where these appointments, events, meetings, and tasks were set, accepted, worked on, and/or completed. In some aspects, the context listener 126 is operable to provide the state of the computing device (e.g., what applications were active, which application resulted in interacting with the task) to the task list service 120. For example, metadata related to whether a user has looked at a given entity part of a task, how long the user has worked on a given task, how long it took between accepting the task and starting or completing the task, and what interactions were made by the user may be gathered for analysis and reporting.
A relational store 127 stores the relations observed for the creation of task items so that dynamic context can be provided to the user when the task is suggested to the user at a later date. For example, when the user manually or a system automatically creates a task item, the task is parsed to locate entities (e.g., persons involved, objects to be acted on) and recent actions (e.g., actions taken in the last m minutes) that may relate to the task item. For example, if the user receives a message containing the phrase “profit sharing plan” and creates a task that also include that phrase, a relationship between the task and the message will be formed and stored in the relational store 127. In another example, when the user creates a task item to meet with another person, a relationship is formed between the task item, the meeting, and the person so that additional information about the meeting or the person can be recalled (e.g., from the relational graph service 130) when the task item is presented to the user. In various aspects, the node identifiers from the relation graph service 130 for related entities are stored in the relational store 127.
The relational graph service 130 hosts a graph database of a relational graph with nodes describing entities and a set of accompanying properties of those entities, such as, for example, the names, titles, ages, addresses, etc. Each property can be considered a key/value pair—a name of the property and its value. In other examples, entities represented as nodes that include documents, meetings, communication, etc., as well as edges representing relations among these entities, such as, for example, an edge between a person node and a document node representing that person's authorship, modification, or viewing of the document. The relational graph service 130 executes graph queries that are submitted by various users to return nodes or edges that satisfy various conditions (e.g., users within the same division of a company, the last X documents accessed by a given user). In various aspects, the relational graph 130 is in communication with the other services 140-160 to match actions to documents and track edges between nodes representing entities from those other services 140-160.
The email service 140 hosts the email communications for one or more users. In various aspects, the email service 140 is part of or includes a directory service for an organization. In other aspects, the email service 140 is integrated into or accessible by a productivity application of the productivity services 160. For example, an email server storing email messages for an organization is accessible by email applications for members of that organizations and acts as an email service 140 accessible by the task list service 120.
Emails provided from the email service 140 may be added as entities in the relational graph 130, and/or the communications embodied by the emails are treated as edges between communicating parties. In various aspects, emails that are part of the tasks (e.g., “send an email to John Doe”) that are monitored by the task list service 120, and also provide context for other tasks, such as, for example, when a task is originated in an email (e.g., an email whose content includes “please review the meeting agenda” originates the task of “review meeting agenda”).
The calendar service 150 hosts calendar and appointment information for one or more users. Various appointments, meetings, and events (collectively, events) are stored in the calendar service 150 that include one or more persons as participants/hosts. Events include one or more of: participants (required or optional), attendance information, times, locations, resources, attached documents, and event information (e.g., event title and description). In various aspects, the calendar service 150 is provided in a unified email/calendar application, such as, for example, THUNDERBIRD® (offered by the Mozilla Fnd of Mountain View, Calif.) or GMAIL® (offered by Alphabet Inc. of Mountain View, Calif.), which stores events for a user of that application. In other aspects, the calendar service 150 includes a social media platform, such as, for example, FACEBOOK® (offered by Facebook, Inc. of Menlo Park, Calif.) where various events are posted that users may attend.
Events provided from the calendar service 150 may be added as entities in the relational graph 130, and/or the interactions embodied by the events are treated as edges between interacting parties. In various aspects, events are part of the tasks (e.g., “attend birthday party”) that are monitored by the task list service 120, and also provide context for other tasks, such as, for example, when a task is originated in an event (e.g., action items created during a meeting).
The productivity service 160 includes one or more productivity applications and document repositories that are accessible by one or more users. In various aspects, the productivity service 160 is hosted on the user device 110 and/or a remote server accessible by the user device 110. For example, the productivity service 160 includes a locally executed authoring application (e.g., PAGES®, KEYNOTE®, or NUMBERS® offered by Apple, Inc. of Cupertino, Calif.) and remotely executed authoring applications (e.g., the GOOGLE DOCS™ suite offered by Alphabet, Inc. of Mountain View, Calif.) that are accessible via a thin client or web browser. In another example, the productivity service 160 include a library of documents stored on the user device 110 as well as libraries stored on networked computers or as part of a document management system and remote storage locations (e.g., GOOGLE DRIVE™ offered by Alphabet, Inc. of Mountain View, Calif.).
Documents provided from the productivity service 160 may be added as entities in the relational graph 130. In various aspects, documents are part of the tasks (e.g., “edit the quarterly report”) that are monitored by the task list service 120, and provide context to report on how tasks have been handled to an initiating or collaborating party. For example, when a manager assigns the task of “edit the quarterly report” to an employee, the manager may receive an indication when the employee has completed the task, and the interactions that comprise that task. Similarly, when a manager assigns the task to a work group of several employees, when one employee assumes the task (e.g., begins work, accepts the task, completes the task), the other employees may be notified that the task has been assumed by their coworker.
In various aspects, the services 130-160 are operable to transmit interactions to the task list service 120 or to have interactions listened to/pulled from the services 130-160 to the task list service 120. An API (Application Program Interface) or agent between the task list service 120 and services 130-160 facilitate communication between the services 130-160 and the task list service 120, ensuring communications are received in a format interpretable by the receiving service. In one example, the SIRI® or GOOGLE NOW® personal digital assistants (offered by Apple, Inc. and Alphabet, Inc., respectively) may parse the sources 130-160 as agents to report relevant data to the task list service 120. In another example, the sources 130-160 are configured to communicate to the task list service 120 as actions are taken in those services 130-160 in a format specified via an API.
FIGS. 2A-10B illustrate various example user interfaces for a task list application employing the tasks list service 120 to improve the efficiency in how tasks are presented to the user. As will be appreciated, FIGS. 2A-10B are provided as non-limiting examples and other arrangements and groupings of user interface elements with different content are included in the concepts of the present disclosure.
FIGS. 2A and 2B are illustrations of example graphical user interfaces in which a Task List is implemented. The task list user interface illustrated in FIG. 2A displays the scheduled tasks for “today”. In one aspect, along with the task item, a link to the application relevant in completing the task item is provided. For example, the second item on the task list user interface displayed in FIG. 2A, is “review notes for launch”. A link to the notes application is provided along with the task item to allow the user to access the notes application from the task list application, instead of the user having to search for the application and then open it.
In one aspect, a link to the content item relevant to completing the task item is provided. For example, the first task item is “prepare screens for presentation”. The task item is provided along with the content item “product_launchdeck” to allow the user to access the content item “product_launchdeck” in the presentation application without having to remember the content item and its location to complete the task item “prepare screens for presentation”.
In one example, the tasks for “today” are listed in the order of time when they are due. In another example, they are listed in the order of priority. According to an example, the priority is identified by the system. In another example, the user is allowed to provide the priority details when creating the task item.
According to an aspect, the task list user interface illustrated in FIG. 2B displays a suggested task list item. As illustrated, a suggested task list item “Book flight to San Francisco” is provided. According to an aspect, the task is suggested based on user context. User context may be developed by the system gradually by learning user patterns and user interaction data. The option to add the suggested task item to the task list shown in FIG. 2A is provided. According to another aspect, the link provides a brief description of the task list item. For example, as illustrated in FIG. 2B, a brief description of the city of San Francisco is provided along with the suggested task list item “Book flight to San Francisco”. In another example, others options such as for example, an option to provide “directions” to the city and an option to “book tickets” are provided.
As illustrated in FIG. 3, the system displays suggested tasks for user to focus on. In the example illustrated in FIG. 3, the first task list item “Check in for your flight” is provided as a focus item for the user. The system identifies a flight reservation in one or more applications accessed by the user, such as the mail application in which the flight confirmation may have arrived. Based on these identified data, the system provides the task list item “Check in for your flight”.
According to another aspect, the system reviews the task list and suggests a task item that may not be due today, as a focus item. For example, if the system identifies a meeting scheduled for Friday, and the task item “prepare for meeting” is scheduled for Wednesday. The system may further identify that there are more task items scheduled for Wednesday than on Tuesday, and the system uses these data to provide the task item, “prepare for meeting” on Tuesday as focus task list item instead of on Wednesday.
As illustrated in FIG. 4A, a calendar application showing one or more events (appointments, meetings, or events) is illustrated in relation to a calendar and order of occurrence for a day.
As illustrated in FIG. 4B, the system provides a Plan of tasks for the user based on data received from the calendar application illustrated in FIG. 4A. As illustrated, the task items that were not completed yesterday from the tasks application are provided to the user today. Further, an interface allowing the user to add a new task is provided. In one aspect, as illustrated in FIG. 4B, the system provides the suggested tasks to the user based on data learned from one or more productivity applications (e.g., the calendar application shown in FIG. 4A) by digital agents, for example, SIRI® (offered by Apple, Inc. of Cupertino Calif.) or ECHO® (offered by Amazon.com, Inc. of Seattle, Wash.), email applications, calendar applications, etc. For example, the suggestion task of “call grandma” is provided from the data learned from the calendar of a task item “Grandma Ruth's Birthday”.
FIG. 5 is an illustration of an example graphical user interface in which Task List implementation is provided. In one aspect, the task list provides a list of task categories. For example as illustrated in FIG. 5, the category list of tasks is provided on the left, showing several categories and subcategories (e.g., “To-Do”, “Groceries”, “School Play”, Launch Event”, “Presentation”, “Reminders”, and “Movies to watch”). Upon selection of one or more categories, such as “presentation”, the task items associated with the category—in this example, “presentation”—are provided in the middle of the illustrated graphical user interface. Further, additional details for task items such as due date, start date, reminders, descriptions, etc., that are associated with the task list, are provided along with the task list items, to the right of those items in the illustrated example.
FIG. 6 is an illustration of a graphical user interface in which a task application provides the task list items in relation to a task list category along with additional data about the selected task list item. For example, as illustrated in FIG. 6 the task items for the task list category “Movies to watch” are displayed for the user. The movies that may be recently watched and completed in the task list are marked completed and shown with a strike though, a check mark, or other indicia of their completion or rejection. Additional information about the selected task item (a movie in this example) is also provided. The additional information may include a brief description of the task item, an option of an application that can be used to interact with the task item, a link to access additional information about the task item in an online encyclopedia or relational graph, etc. In one example, the system provides the user with the due date data and allows the user to add the selected task item to a time period list (e.g., “today's tasks”). In one example, an option to create a new task item is also provided. Further, another option to delete the selected task item is also provided.
FIG. 7 is an illustration of a graphical user interface in which task list templates are provided to allow the users to get specifics information about a content item provided as a task item template, such as, for example, a job or a location. These task list templates can be used repeatedly for multiple users that are new to that job or location. As a result of task list application and the template's availability across all major platforms, it makes accessing actionable information at the right time and place easier for the user, improving their productivity. For example, the job “ABC-CITICO-450” illustrated in FIG. 7 is associated with a template task list, and the task list items included in the “ABC-CITICO-450” task list template help a user working on it to access relevant information in fewer steps and with lower risk of omitting a step.
FIG. 8 illustrates various source attributions for suggested tasks items. As shown in FIG. 8, the task sources from which suggested tasks are drawn and the agent or suggestion engine 122 that is used to identify those suggested tasks is presented to the user. Various icons and source names are also presented in association with the suggested tasks items to alert the user as to where, and potentially why, a given suggested task item is presented as a suggestion. For example, text from an email message may be extracted as promising a task item that is discovered by a context listener 126 and is therefore presented with an email icon along with the text that cause the context listener 126 to infer that a task item exists. In another example, a task in a user's task list for a later date is presented with a task list icon, and is suggested due to a surplus of inferred time on the user's schedule for the current date (e.g., today is open and the task item is due tomorrow). In a further example, a calendar icon is presented in association with an inferred task list item discovered from a calendar application.
FIG. 9 illustrates an example user interface for a digital assistant to create task items. As illustrated in FIG. 9, a digital assistant has been invoked and provides various controls for defining a task item. In various aspects, user input may be received by the digital assistant from input devices such as keyboards, mice, and touch screens, but also from microphones when the digital assistant is operable to interpret speech for content input and commands. The digital assistant may be invoked by the user uttering a command sequence to summon the digital assistant, selecting a control in a user interface, or automatically in response to user actions in a productivity application, task list application, or operating system.
Input fields include, but are not limited to, title, description, persons involved, places involved, and times involved fields. The user is operable to set which task list the task item is added to, or the system may automatically add the task item to a task list according to a determination of common subject matter, time, or location according to a clusterer 123. Additional controls are provided for the user to accept the creation of the task item (e.g., “remind”), reject the creation of the task item (e.g., “cancel”), and to locate additional data related to the task item (e.g., “search for . . . ”).
FIGS. 10A and 10B illustrate example user interfaces for a time period view of a set of task items as the time period's task list is constructed. FIG. 10A illustrates a starting position for a time period, a daily task list in the present example, which is blank when the time period list initiates. The blank view allows the user control over which candidate tasks for the day appear on the day's task list; suggested task items are shown but are not included on the list until selected by the user. In various aspects, the blank view is presented to the user at the start of the time period or at the conclusion of the prior time period. Although a user may pre-plan several tasks, in various aspects, the time period view is only accessible in the time period directly before or during the time period associated with the time period list (e.g., that day or the day before for a daily list, at the start of a week or the end of the prior week for a weekly list, etc.).
Proceeding from FIG. 10A, the interface illustrated in FIG. 10B shows several suggested tasks for the time period as they are provided to the user. As shown in an upper portion in the interface, tasks from the prior time period (e.g., yesterday in a daily task list) are shown to the user so that incomplete tasks may be selected for inclusion in the current time period task list or the user is reminded of what was completed tasks were accomplished in the prior time period to create ongoing tasks. As shown in a middle portion of the interface, tasks that are overdue are shown to the user so that overdue tasks items may be selected for inclusion in the current time period task list. As shown in a lower portion of the interface, suggested task items from one or more services 130-160 as selected by a suggestion engine 120 are shown to the user to select from for inclusion in the time period list.
Various details about the suggested task items are shown to the user, including, without limitation: a title, a description, interested or relevant parties (e.g., assignor, assignee, sender, receiver, resource), due dates, start dates, portion already completed, sub-tasks, and related objects. Controls are provided in the interface in association with the suggested tasks to select one or more of the suggested task items to add an existing task list or new task list. Controls are also provided for the user to manually add task items to an existing or new task list. In other aspects, controls are provided to reject suggested task items, and the suggestion engine 122 is operable, in some aspects, to replace the rejected task items with other suggested task items. The heuristic engine 121 is operable to learn the user's behavior based on the user's interactions (e.g., selection, rejecting, ignoring) with the presented task items to improve the task items that the suggestion engine 122 provides.
FIG. 11 is a flow chart showing general stages involved in an example method 1100 for enhancing efficiency by clustering task items by their interaction contexts to provide more relevant results to users at different times and locations. The user's behavior is learned, based on how the user has interacted with the task list application, to provide tasks reminders or suggested tasks at a given time/location based on that learned behavior relative to the user's current context, time and location. For example, before going to work, the user may be at home, and because inputting work tasks into the task list application that may be based on the semantic context of the task items and/or historical data indicating that task items input at this time and location may be completed at a different time and location (e.g., at the office), it may be determined to display those task items during work hours and/or at a work location. Similarly, although the user is at home and inputting tasks outside of work hours associated with the user, suggested tasks for work items are presented by the suggestion engine 122 based on the user pre-populating a work task list before heading into work.
Method 1100 begins at OPERATION 1110, where task items are clustered based on similar times of interaction, locations of interaction, and content interacted with (e.g., semantically parsing emails or documents for key terms) the task items or related contextual objects. As will be appreciated, various clustering algorithms are used to determine any distinct groupings of task items, such as, for example, workday professional tasks, workday personal tasks, and weekend tasks. The system learns these clusters, but it does not need to know of their context (e.g., home versus work), just that certain tasks are interacted with in certain ways in certain locations and at certain times. The clusterer 123 clusters those task lists, task items, and keywords/concepts together into semantic concepts so that when the user is in the location/at the time when the task application is loaded, or uses related terminology, the heuristic engine 121 has learned over time based on user input that certain task items fall into certain clustered categories.
For example, a user with a task list labeled “songs I want to learn on guitar” is noted as interacting with the task list while at a first location (e.g., home) and during non-working hours will have that task list clustered towards being relevant while at the first location (e.g., home) and during non-working hours. Machine learning techniques, such as vector space search techniques, LDA (Latent Dirichlet Allocation) modeling, etc., are used to automatically create clusters on key words (e.g., task item names, document titles), their frequency of use, and multidimensional geographic proximity to determine that clusters and/or tasks are related. For example, when a user has input music related terms for task items, those terms will tend to overlap more frequently in text so that the clusterer 123 will determine that documents including terms, such as, for example, “notes,” “songs,” “intonation,” etc., related to music and also relate to the task list “songs I want to learn on guitar,” even though those terms might not all exist at the same time in each related document.
Additionally, as a user may group a plurality of task items into a task list, the clusterer 123 is further operable to determine that one task item belongs to a given cluster that the other task items on that task list should also belong to that given cluster. For example, for a “grocery shopping” task list, the user may input various foods to buy as individual task items. If the clusterer 123 has sufficient semantic context to add a first task item (e.g., “buy milk”) to a given cluster, but insufficient semantic context to add a second task item (e.g., “buy artichokes”) to that cluster, the presence of the two tasks in the same task list enable the clusterer 123 to cluster them together, despite any insufficiency in semantic context. The clusterer 123 is enabled to learn from this grouping so that if the user were to create a new “grocery shopping list” without the first task item, but with the second task item, the second task item (and the remaining task items from the task list) will be assigned the semantic cluster for “grocery shopping”.
At OPERATION 1120, the time and location of accessing the task list application is determined. A user accesses the task list application not only by calling up a task list in a dedicated interface, but leaving a task list application active (e.g., as a background processes) to provide notifications, alerts, or alarms to provide contextually relevant tasks items in response to the location or time. In various aspects, the location of the user device 110 is determined based on GPS data from a GPS transponder of the user device 110 (if one is installed), IP location services for the IP address of the user device 110, and/or the identity of the network to which the user device 110 is connected or that are available to connect to. The time of access is determined in various aspects based on a system clock included in the user device 110 or referencing an external time source.
Proceeding to OPERATION 1130, relevant clusters of tasks for the current time of access and/or location of the user device 110 are identified. The time and a location associated with each cluster created in OPERATION 1110 is compared to the time and locations that are current for the user device 110 to match these times and locations to events in the task list or to present a contextually appropriate task list (or prevent a contextually inappropriate task list from being displayed). For example, a user who is checking a task list while at work may not want to have personal tasks displayed (e.g., due to including embarrassing personal details, distracting from work-related tasks), but will want those task items displayed when the user is not at work. In another aspect, as time progresses, it may be determined to provide tasks that belong in a cluster for the next time period.
For example, if the user is at a work location leading up to the end of a time associated with work activities, it may be determined to begin showing tasks associated with a non-work location and non-working times. A transition from one cluster to another may be sudden, or it may be gradual, in which a few items relevant to a second cluster are provided during a time/location of a first cluster, and are gradually replaced from the first cluster as time progresses. For example, as the user's normal workday draws to the close, or extends into overtime, tasks items relevant to personal tasks will begin to replace workplace tasks, despite the user remaining in a work-location.
In another example, where a user is at a work location at a non-work related time, such as when going into on a weekend, it may be determined to show the user tasks associated with the work location, despite the time of access being a non-work related time. Depending on user preferences (e.g., stored in a user profile 125), when a time context and a location context are matched to different clusters, tasks from both clusters may be identified for presentation to the user, tasks from clusters of a preferred context (locational or temporal) are identified for presentation to the user, or a context preference is used to weight which clusters have more (or fewer) tasks selected for presentation to the user. In various aspects, as candidate task items are presented to the user when an ambiguous context (i.e., the time context and the location context are matched to different clusters) is detected, the user is enabled to accept (or reject) the candidate task items, which will provide more (or fewer tasks) items from the indicated cluster.
When a location for a user device 110 is not recognized as corresponding to an existing cluster, the task list service 120 is operable to fall back to time as the only context to select a given cluster. The clusterer 123 tracks the new location and if there are enough data points in the new location, a new location cluster may be developed or an existing cluster may be associated with one or more (new) locations. For example, if the user leaves a workplace early and continues working at a coffee shop that has not be visited before, the cluster 123 is initially operable to use the time (e.g., “work hours”) to provide work related tasks to the user. As the user's actions are monitored, the coffee shop's new location may be associated with a new task list, an existing working task list, or a subset of an existing working task list's task items to reflect the user's behavior at the coffee shop relative to task items. For example, if the user leaves a workplace and replies to several emails towards the end of the workday or writes documents (free from workplace distractions), the clusterer 123 is operable to note the tasks that the user performs at that environment and/or time.
Each cluster has a known context, such that the clusterer 123 interprets that a user is at a first location, a second location, etc., during a first time range, a second time range, etc., to identify different clusters based on when the users are interacting with the task list application and the services 130-160. The clusterer 123 does not need to know the identity of these locations (e.g., workplace, home, gym, coffee shop, etc. are treated as locations one, two, three, four, etc.) or time ranges (e.g., workhours, off-hours, weekend, weekday, etc. are treated as time ranges one, two, three, four, etc.), but a user may supply context for these clustering criteria that the clusterer 123 may use to identify various activities or clusters (e.g., office during work hours, office afterhours, home-headed to work, home-headed to gym, home-returned).
In another aspect, a new cluster may be developed when a new time period (or new actions in an already seen time period) is observed, in which the task list service 120 is operable to fall back to location as the only context to select a given cluster. For example, a user may begin waking up earlier to incorporate a new exercise regime before leaving for work. In another example, a user may carve out a portion of the day for performing a new set of tasks, such as, when the user decides for personal growth to start doing research on a certain topic every Tuesday and Thursday during a lunch hour normally associated with “work hours” while located at a location associated as a “workplace.” The clusterer 123, in coordination with the heuristic engine 121, learns that there is a time slot where there is a cluster of tasks that are distinct from the rest of the tasks and will learn that Tuesday and Thursday during lunch is a special time for the user, even though the location has not changed.
Method 1100 proceeds to OPERATION 1140 where tasks from the relevant clusters, based on the available clusters having one or more contextual data for time of access or location of access in common with the current time of access or location of access are presented to the user device 110 for display and/or suggestion via the task list application. In various aspects, the task items from a relevant cluster come from one or more task lists (e.g., an “after work hours” cluster includes a “household chores” list and a “grocery shopping” list) and all or a portion of the available task items. A task list, or a title thereof, is presented in addition or instead of the task items thereon. For example, a task list for “grocery shopping” may be presented instead of or in addition to component task items of “buy milk” and “buy bread”, and a title of a task list may be substituted for its components tasks when at least a user-configurable number of task items from that list are selected for presentation as candidate task items. Method 1100 may then conclude.
While implementations have been described in the general context of program modules that execute in conjunction with an application program that runs on an operating system on a computer, those skilled in the art will recognize that aspects may also be implemented in combination with other program modules. Generally, program modules include routines, programs, components, data structures, and other types of structures that perform particular tasks or implement particular abstract data types.
The aspects and functionalities described herein may operate via a multitude of computing systems including, without limitation, desktop computer systems, wired and wireless computing systems, mobile computing systems (e.g., mobile telephones, netbooks, tablet or slate type computers, notebook computers, and laptop computers), hand-held devices, multiprocessor systems, microprocessor-based or programmable consumer electronics, minicomputers, and mainframe computers.
In addition, according to an aspect, the aspects and functionalities described herein operate over distributed systems (e.g., cloud-based computing systems), where application functionality, memory, data storage and retrieval and various processing functions are operated remotely from each other over a distributed computing network, such as the Internet or an intranet. According to an aspect, user interfaces and information of various types are displayed via on-board computing device displays or via remote display units associated with one or more computing devices. For example, user interfaces and information of various types are displayed and interacted with on a wall surface onto which user interfaces and information of various types are projected. Interaction with the multitude of computing systems with which implementations are practiced include, keystroke entry, touch screen entry, voice or other audio entry, gesture entry where an associated computing device is equipped with detection (e.g., camera) functionality for capturing and interpreting user gestures for controlling the functionality of the computing device, and the like.
FIGS. 12-14 and the associated descriptions provide a discussion of a variety of operating environments in which examples are practiced. However, the devices and systems illustrated and discussed with respect to FIGS. 12-14 are for purposes of example and illustration and are not limiting of a vast number of computing device configurations that are utilized for practicing aspects, described herein.
FIG. 12 is a block diagram illustrating physical components (i.e., hardware) of a computing device 1200 with which examples of the present disclosure may be practiced. In a basic configuration, the computing device 1200 includes at least one processing unit 1202 and a system memory 1204. According to an aspect, depending on the configuration and type of computing device, the system memory 1204 comprises, but is not limited to, volatile storage (e.g., random access memory), non-volatile storage (e.g., read-only memory), flash memory, or any combination of such memories. According to an aspect, the system memory 1204 includes an operating system 1205 and one or more program modules 1206 suitable for running software applications 1250. According to an aspect, the system memory 1204 includes the task list service 120. The operating system 1205, for example, is suitable for controlling the operation of the computing device 1200. Furthermore, aspects are practiced in conjunction with a graphics library, other operating systems, or any other application program, and are not limited to any particular application or system. This basic configuration is illustrated in FIG. 12 by those components within a dashed line 1208. According to an aspect, the computing device 1200 has additional features or functionality. For example, according to an aspect, the computing device 1200 includes additional data storage devices (removable and/or non-removable) such as, for example, magnetic disks, optical disks, or tape. Such additional storage is illustrated in FIG. 12 by a removable storage device 1209 and a non-removable storage device 1210.
As stated above, according to an aspect, a number of program modules and data files are stored in the system memory 1204. While executing on the processing unit 1202, the program modules 1206 (e.g., task list service 120) perform processes including, but not limited to, one or more of the stages of the method 1100 illustrated in FIG. 11. According to an aspect, other program modules are used in accordance with examples and include applications such as electronic mail and contacts applications, word processing applications, spreadsheet applications, database applications, slide presentation applications, drawing or computer-aided application programs, etc.
According to an aspect, the computing device 1200 has one or more input device(s) 1212 such as a keyboard, a mouse, a pen, a sound input device, a touch input device, etc. The output device(s) 1214 such as a display, speakers, a printer, etc. are also included according to an aspect. The aforementioned devices are examples and others may be used. According to an aspect, the computing device 1200 includes one or more communication connections 1216 allowing communications with other computing devices 1218. Examples of suitable communication connections 1216 include, but are not limited to, radio frequency (RF) transmitter, receiver, and/or transceiver circuitry; universal serial bus (USB), parallel, and/or serial ports.
The term computer readable media, as used herein, includes computer storage media. Computer storage media include volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information, such as computer readable instructions, data structures, or program modules. The system memory 1204, the removable storage device 1209, and the non-removable storage device 1210 are all computer storage media examples (i.e., memory storage.) According to an aspect, computer storage media include RAM, ROM, electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, CD-ROM, digital versatile disks (DVD) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other article of manufacture which can be used to store information and which can be accessed by the computing device 1200. According to an aspect, any such computer storage media is part of the computing device 1200. Computer storage media do not include a carrier wave or other propagated data signal.
According to an aspect, communication media are embodied by computer readable instructions, data structures, program modules, or other data in a modulated data signal, such as a carrier wave or other transport mechanism, and include any information delivery media. According to an aspect, the term “modulated data signal” describes a signal that has one or more characteristics set or changed in such a manner as to encode information in the signal. By way of example, and not limitation, communication media include wired media such as a wired network or direct-wired connection, and wireless media such as acoustic, radio frequency (RF), infrared, and other wireless media.
FIGS. 13A and 13B illustrate a mobile computing device 1300, for example, a mobile telephone, a smart phone, a tablet personal computer, a laptop computer, and the like, with which aspects may be practiced. With reference to FIG. 13A, an example of a mobile computing device 1300 for implementing the aspects is illustrated. In a basic configuration, the mobile computing device 1300 is a handheld computer having both input elements and output elements. The mobile computing device 1300 typically includes a display 1305 and one or more input buttons 1310 that allow the user to enter information into the mobile computing device 1300. According to an aspect, the display 1305 of the mobile computing device 1300 functions as an input device (e.g., a touch screen display). If included, an optional side input element 1315 allows further user input. According to an aspect, the side input element 1315 is a rotary switch, a button, or any other type of manual input element. In alternative examples, mobile computing device 1300 incorporates more or fewer input elements. For example, the display 1305 may not be a touch screen in some examples. In alternative examples, the mobile computing device 1300 is a portable phone system, such as a cellular phone. According to an aspect, the mobile computing device 1300 includes an optional keypad 1335. According to an aspect, the optional keypad 1335 is a physical keypad. According to another aspect, the optional keypad 1335 is a “soft” keypad generated on the touch screen display. In various aspects, the output elements include the display 1305 for showing a graphical user interface (GUI), a visual indicator 1320 (e.g., a light emitting diode), and/or an audio transducer 1325 (e.g., a speaker). In some examples, the mobile computing device 1300 incorporates a vibration transducer for providing the user with tactile feedback. In yet another example, the mobile computing device 1300 incorporates input and/or output ports, such as an audio input (e.g., a microphone jack), an audio output (e.g., a headphone jack), and a video output (e.g., a HDMI port) for sending signals to or receiving signals from an external device. In yet another example, the mobile computing device 1300 incorporates peripheral device port 1340, such as an audio input (e.g., a microphone jack), an audio output (e.g., a headphone jack), and a video output (e.g., a HDMI port) for sending signals to or receiving signals from an external device.
FIG. 13B is a block diagram illustrating the architecture of one example of a mobile computing device. That is, the mobile computing device 1300 incorporates a system (i.e., an architecture) 1302 to implement some examples. In one example, the system 1302 is implemented as a “smart phone” capable of running one or more applications (e.g., browser, e-mail, calendaring, contact managers, messaging clients, games, and media clients/players). In some examples, the system 1302 is integrated as a computing device, such as an integrated personal digital assistant (PDA) and wireless phone.
According to an aspect, one or more application programs 1350 are loaded into the memory 1362 and run on or in association with the operating system 1364. Examples of the application programs include phone dialer programs, e-mail programs, personal information management (PIM) programs, word processing programs, spreadsheet programs, Internet browser programs, messaging programs, and so forth. According to an aspect, the task list service 120 is loaded into memory 1362. The system 1302 also includes a non-volatile storage area 1368 within the memory 1362. The non-volatile storage area 1368 is used to store persistent information that should not be lost if the system 1302 is powered down. The application programs 1350 may use and store information in the non-volatile storage area 1368, such as e-mail or other messages used by an e-mail application, and the like. A synchronization application (not shown) also resides on the system 1302 and is programmed to interact with a corresponding synchronization application resident on a host computer to keep the information stored in the non-volatile storage area 1368 synchronized with corresponding information stored at the host computer. As should be appreciated, other applications may be loaded into the memory 1362 and run on the mobile computing device 1300.
According to an aspect, the system 1302 has a power supply 1370, which is implemented as one or more batteries. According to an aspect, the power supply 1370 further includes an external power source, such as an AC adapter or a powered docking cradle that supplements or recharges the batteries.
According to an aspect, the system 1302 includes a radio 1372 that performs the function of transmitting and receiving radio frequency communications. The radio 1372 facilitates wireless connectivity between the system 1302 and the “outside world,” via a communications carrier or service provider. Transmissions to and from the radio 1372 are conducted under control of the operating system 1364. In other words, communications received by the radio 1372 may be disseminated to the application programs 1350 via the operating system 1364, and vice versa.
According to an aspect, the visual indicator 1320 is used to provide visual notifications and/or an audio interface 1374 is used for producing audible notifications via the audio transducer 1325. In the illustrated example, the visual indicator 1320 is a light emitting diode (LED) and the audio transducer 1325 is a speaker. These devices may be directly coupled to the power supply 1370 so that when activated, they remain on for a duration dictated by the notification mechanism even though the processor 1360 and other components might shut down for conserving battery power. The LED may be programmed to remain on indefinitely until the user takes action to indicate the powered-on status of the device. The audio interface 1374 is used to provide audible signals to and receive audible signals from the user. For example, in addition to being coupled to the audio transducer 1325, the audio interface 1374 may also be coupled to a microphone to receive audible input, such as to facilitate a telephone conversation. According to an aspect, the system 1302 further includes a video interface 1376 that enables an operation of an on-board camera 1330 to record still images, video stream, and the like.
According to an aspect, a mobile computing device 1300 implementing the system 1302 has additional features or functionality. For example, the mobile computing device 1300 includes additional data storage devices (removable and/or non-removable) such as, magnetic disks, optical disks, or tape. Such additional storage is illustrated in FIG. 13B by the non-volatile storage area 1368.
According to an aspect, data/information generated or captured by the mobile computing device 1300 and stored via the system 1302 are stored locally on the mobile computing device 1300, as described above. According to another aspect, the data are stored on any number of storage media that are accessible by the device via the radio 1372 or via a wired connection between the mobile computing device 1300 and a separate computing device associated with the mobile computing device 1300, for example, a server computer in a distributed computing network, such as the Internet. As should be appreciated such data/information are accessible via the mobile computing device 1300 via the radio 1372 or via a distributed computing network. Similarly, according to an aspect, such data/information are readily transferred between computing devices for storage and use according to well-known data/information transfer and storage means, including electronic mail and collaborative data/information sharing systems.
FIG. 14 illustrates one example of the architecture of a system for improving the efficiency of managing task lists as described above. Content developed, interacted with, or edited in association with the task list service 120 is enabled to be stored in different communication channels or other storage types. For example, various documents may be stored using a directory service 1422, a web portal 1424, a mailbox service 1426, an instant messaging store 1428, or a social networking site 1430. The task list service 120 is operative to use any of these types of systems or the like for improving efficiency of task list management, as described herein. According to an aspect, a server 1420 provides the task list service 120 to clients 1405 a,b,c. As one example, the server 1420 is a web server providing the task list service 120 over the web. The server 1420 provides the task list service 120 over the web to clients 1405 through a network 1440. By way of example, the client computing device is implemented and embodied in a personal computer 1405 a, a tablet computing device 1405 b or a mobile computing device 1405 c (e.g., a smart phone), or other computing device. Any of these examples of the client computing device are operable to obtain content from the store 1416.
Implementations, for example, are described above with reference to block diagrams and/or operational illustrations of methods, systems, and computer program products according to aspects. The functions/acts noted in the blocks may occur out of the order as shown in any flowchart. For example, two blocks shown in succession may in fact be executed substantially concurrently or the blocks may sometimes be executed in the reverse order, depending upon the functionality/acts involved.
The description and illustration of one or more examples provided in this application are not intended to limit or restrict the scope as claimed in any way. The aspects, examples, and details provided in this application are considered sufficient to convey possession and enable others to make and use the best mode. Implementations should not be construed as being limited to any aspect, example, or detail provided in this application. Regardless of whether shown and described in combination or separately, the various features (both structural and methodological) are intended to be selectively included or omitted to produce an example with a particular set of features. Having been provided with the description and illustration of the present application, one skilled in the art may envision variations, modifications, and alternate examples falling within the spirit of the broader aspects of the general inventive concept embodied in this application that do not depart from the broader scope.

Claims

We claim:

1. A method for clustering task items by interaction contexts to provide more relevant results to users at different times and locations, comprising:

clustering task items for a user into clusters based on times and locations of the task items;

in response to a user device accessing a task list service, determining a time of access and a location of the user device at the time of access;

identifying relevant clusters from the clusters based on the time of access and the location of the user device; and

presenting task items from the relevant clusters on the user device.

2. The method of claim 1, wherein clustering the task items further comprises:

observing user actions relative to the task items to determine the times and the locations of the task items.

3. The method of claim 1, wherein clustering the task items further comprises:

observing key words of the task items; and

clustering the task items in association with the key words.

4. The method of claim 1, wherein clustering the task items further comprises:

observing task lists comprising pluralities of task items; and

in response to adding one task item from a given task list to a given cluster, adding remaining tasks items of the given task list to the given cluster.

5. The method of claim 1, wherein determining the location of the user device further is based on at least one of:

Global Positioning System data for the user device;

Internet Protocol location services related to an IP address of the user device; or

an identity of a network to which the user device is connected.

6. The method of claim 1, wherein identifying the relevant clusters further comprises:

receiving user input rejecting a given task item presented on the user device from a given relevant cluster; and

in response to the user input rejecting the given task item, presenting a new task item from a different relevant cluster.

7. The method of claim 1, wherein presenting task items from the relevant clusters further comprises:

determining whether a user-configurable number of the task items from the relevant clusters belong to a given task list; and

in response to determining that a user-configurable number of the task items from the relevant clusters belong to the given task list, substituting a name of the given task list for the task items for presentation on the user device.

8. A system for clustering task items by interaction contexts to provide more relevant results to users at different times and locations, comprising:

a processor; and

a computer readable memory storage device including instructions that when executed by the processor enable the system to:

cluster task items for a user into clusters based on times and locations of the task items;

in response to a user device accessing a task list service, determine a time of access and a location of the user device at the time of access;

identify relevant clusters from the clusters based on the time of access and the location of the user device; and

present task items from the relevant clusters on the user device.

9. The system of claim 8, wherein to cluster the task items the system is further operable to:

observe user actions relative to the task items to determine the times and the locations of the task items

10. The system of claim 8, wherein to cluster the task items the system is further operable to:

observe key words of the task items; and

cluster the task items in association with the key words.

11. The system of claim 8, wherein to cluster the task items the system is further operable to:

observe task lists comprising pluralities of task items; and

in response to adding one task item from a given task list to a given cluster, adding remaining tasks items of the given task list to the given cluster

12. The system of claim 8, wherein to determine the location of the user device the system is uses at least one of:

Global Positioning System data for the user device;

an identity of a network to which the user device is connected.

13. The system of claim 8, wherein to identify the relevant clusters the system is further operable to:

receive user input rejecting a given task item presented on the user device from a given relevant cluster; and

in response to the user input rejecting the given task item, reject the given cluster as a relevant cluster.

14. The system of claim 8, wherein to present task items from the relevant clusters the system is further operable to:

determine whether a user-configurable number of the task items from the relevant clusters belong to a given task list; and

in response to determining that a user-configurable number of the task items from the relevant clusters belong to the given task list, substituting a name of the given task list for the task items for presentation on the user device

15. A computer readable storage device including processor executable instructions for clustering task items by interaction contexts to provide more relevant results to users at different times and locations, comprising:

presenting task items from the relevant clusters on the user device.

16. The computer readable storage device of claim 15, wherein clustering the task items further comprises:

17. The computer readable storage device of claim 15, wherein clustering the task items further comprises:

observing key words of the task items; and

clustering the task items in association with the key words.

18. The computer readable storage device of claim 15, wherein clustering the task items further comprises:

observing task lists comprising pluralities of task items; and

19. The computer readable storage device of claim 15, wherein identifying the relevant clusters further comprises:

20. The computer readable storage device of claim 15, wherein presenting task items from the relevant clusters further comprises: