Contextually-aware recommendations for assisting users with task completion

A system for assisting users in making progress towards the completion of a task by recommending actions is provided. The techniques disclosed herein enable a system to receive user input signals, such as a voice input, a text input or any other type of input comprising phrases indicating a task. The system can then automatically generate recommendations for directing the user to complete the task. The techniques disclosed herein can also identify a contextually-relevant application that is most suitable for completing the identified task. For instance, a user may have several messaging applications. Based on a context of the user's input, and other historical data utilized by one or more machine learning engines, a system can recommend an appropriate application, or provide a ranking of the applications, suitable for completing an identified task. The techniques disclosed herein also employ machine learning engines to improve accuracy of the recommendations over time.

BACKGROUND

In many productivity applications, such as Outlook, Word, Excel, OneNote, Pages, Numbers, Google Docs, and Google Sheets, each application has specialized features enabling users to communicate, manipulate, and otherwise process data. Although productivity applications can help users perform many different tasks, the complexity of each application grows as more features are added to each application. In some instances, a user may not know how to perform a particular task given all of the features of a particular application. Thus, some applications may not provide the best user experience or allow a user to fully benefit from its available features.

Some existing systems can assist users with some applications. For instance, Apple provides a voice-activated service that enables users to initiate phone calls or send text messages by the use of a voice command. Although these types of services are helpful in assisting users to make calls or send messages, these systems are static in nature, as they only allow for the use of a single application for a particular task. This limitation does not always provide the best user experience. For example, when a user says the command “Call Michael,” the service initiates a phone call. Unfortunately, even if a user has a number of available communication applications to make a call, such services only use the phone application. Such existing services are not dynamic in nature, e.g., some services cannot identify and utilize a contextually-relevant application based on the user's circumstances. Such shortcomings can lead to the underutilization of applications, the underutilization of features within an application, etc.

SUMMARY

The techniques disclosed herein enable a system to assist a user in making progress towards the completion of a task by automating and/or recommending actions based on user input signals. The input signals can include a voice input, a text input, or any other type of input that includes phrases indicating a task. The input signals can be interpreted by system to identify a user's intent. Based on data defining a user's intent, the system can generate data defining a number of actions to complete the task. The device can then automatically execute the actions for the user or generate one or more recommendations for directing the user to manually complete the task. The techniques disclosed herein can also identify a contextually-relevant application that is most suitable for completing the identified task. For instance, a user may have several messaging applications, e.g., a first application for personal messages, a second application for work messages, and a third application for volunteer work. Based on a context, e.g., a user's intent, of the user's input, and other contextual data, including historical data processed by one or more machine learning engines, a system can recommend an appropriate application, or provide a ranking of the applications, suitable for completing an identified task.

In one illustrative example, an input signal to a computing device can indicate that a user wants to “Send an email to Katie about a design sync.” The computing device utilizes the input signal to generate data indicating a task to be done, e.g., the task of sending an email. The task can be determined by the analysis of a phrase or a set of keywords. The computing device can then select and rank appropriate applications suitable for performing the task. In some embodiments, applications are ranked based on an analysis of the input signal and/or contextual data. The contextual data can include any stored information with respect to a user's activity, such as a user's location, a date, and/or a time of day. The contextual data can also include a user's historical activity data that is collected and processed by a machine learning engine. A list of the ranked applications can be displayed to a user for selection.

The computing device can also generate data defining a list of actions associated with the task. In the present example, the actions can include instructions or code for causing a computer to execute a selected email application, and populate one or more fields within the email application, such as an email address, subject line, etc. The data that is utilized to populate fields within an application can be retrieved from the input signals, or such data can also be retrieved from other resources such as a database, a mail server, a remote storage service, etc. Based on the input signal and/or other contextual data, the computing device can also identify and recommend one or more attachments to include in the email, language to be used in the email, etc. The list of actions can be displayed to the user to enable the user to complete a task, or the list of actions can be used by the computing device to automatically complete the list of actions. The content including the list of actions available to be performed can be displayed to the user, and such content can be in any format that helps the user progress towards the completion of a given identified task. The content, for instance, can include text, audio data, and/or video data. The content can also include code or data for causing an application or a system to automate one or more actions.

The techniques disclosed herein also utilize machine learning techniques for selecting and/or ranking recommendations for the task to be performed. For instance, a confidence score can be generated based on a user's past patterns. The confidence score can then be used to rank recommendations. As will be described in more detail below, this feature enables the system to provide dynamically adjusted rankings of recommended applications that are based on past identified user activity. Such machine learning techniques can be used to develop an understanding of the intentions of a user and/or one or more selected actions that are possible for completing a task. The features disclosed herein enable a computing device to provide dynamically-aware recommendations that adjust to a user's circumstances and changing scenarios. The features disclosed herein also help users with the utilization of a broad range of applications, and help users utilize and discover features within an application.

It should be appreciated that the above-described subject matter may be implemented as a computer-controlled apparatus, a computer process, a computing system, or as an article of manufacture such as a computer-readable storage medium. Among many other benefits, the techniques described herein improve efficiencies with respect to a wide range of computing resources. For instance, human interaction with a device may be improved as the use of the techniques disclosed herein enable a user to view a wide range of file types available for task performance while staying on one Website. In addition, improved human interaction improves other computing resources such as processor and network resources, e.g., users can stay on one Website and reduce interaction, traffic and computer cycles. Other technical effects other than those mentioned herein can also be realized from implementations of the technologies disclosed herein.

DETAILED DESCRIPTION

The techniques disclosed herein enable a computer system to assist a user in making progress towards the completion of a task to be performed by automating and/or recommending actions based on user input signals. The input signals can include a voice input, a text input or any other type of input including phrases indicating a task. The input signals can be interpreted by the system to identify a user's intent. Based on a user's intent, the system can generate data defining a number of available actions to complete the task. The device can then automatically execute the actions for the user or generate one or more recommendations for directing the user to manually complete the task. The techniques disclosed herein can also identify a contextually-relevant application that is most suitable for completing the identified task. For instance, a user may have several messaging applications, e.g., a first application for personal messages, a second application for work messages, and a third application for volunteer work. Based on a context of the user's input, and other historical data utilized by one or more machine learning engines, a system can recommend an appropriate application, or provide a ranking of the available applications, suitable for completing an identified task.

It should be appreciated that the subject matter described herein may be implemented as a computer-controlled apparatus, a computer process, a computing system, or as an article of manufacture such as a computer-readable storage medium. Among many other benefits, the techniques described herein improve efficiencies with respect to a wide range of computing resources. For instance, human interaction with a device may be improved as the use of the techniques disclosed herein enable a user to gain an understanding of the applications that can be utilized for a particular task. The techniques disclosed herein can also display the best applications that can be utilized for performing a particular task. Such features provide improvements over existing systems in that a user may discover a more optimal application or service for performing a task, which can save a number of computing resources including network bandwidth, processing power, memory usage, etc. Other technical effects other than those mentioned herein can also be realized from implementations of the technologies disclosed herein.

Turning now toFIGS. 1A-1I, a number of screen shots are used to illustrate an example scenario utilizing the techniques disclosed herein. This example involves a scenario where a user interacts with a mobile device. Although a mobile device is utilized in this example, it can be appreciated that the techniques disclosed herein can apply to any computing device (such as the computing devices600inFIG. 6, the client devices706inFIG. 7, and the computing device800shown inFIG. 8), such as a tablet, laptop, etc. In this example, a user interacts with a task application to generate task items. Applications can be recommended to the user based on the user input signals. Although recommended applications are used to illustrate aspects of the present disclosure, it can be appreciated that the techniques disclosure herein can recommend and/or rank services, which may be executed on a local device or a remote device, or other types of software modules such as plug-ins, scripts, macros, etc. As shown in the corresponding figures and described below, the applications are dynamically recommended and ranked based on the input signals and other contextual data.

FIG. 1Ais a screenshot of a user interface101displaying a task list102. To add a task item to the task list102, a user can select the “Add Task” graphical element. In response to the selection, as shown inFIG. 1B, the device displays a text field103and a recommendation field104.

As shown inFIG. 1C, the user begins providing an input signal. In this illustrative example, the user enters the word “compose.” In response to receiving this input signal, the computing device analyzes the word to determine an intent of the user. In some configurations, the word “compose” and other keywords can be associated with one or more applications. By an association between keywords and one or more applications, the computing device can select and recommend a number of applications appropriate for carrying out a task associated with the given word. As disclosed herein, the selected applications are provided as a recommendation to the user.

In some configurations, the selected applications are ranked according to a confidence score. A confidence score can be determined using a number of factors. For instance, a confidence score can be based on keywords that are provided in the input signal. In addition, a confidence score can be based on history data indicating user activity. The history data may describe a number of times a user has selected a particular application, or a number of times the user has selected a particular application in association with a particular keyword. Other contextual data and any suitable machine learning algorithms can be utilized to determine a confidence score for each individual application. The individual applications can be ranked based on a confidence score.

In the example shown inFIG. 1C, the recommended applications are ranked based on the confidence score; the application with the highest confidence score is positioned on the left, followed by applications that are arranged by the confidence score, the application with the lowest confidence score positioned on the right. In this example, with a single word entered by the user, the recommendation field104lists a ranked order of applications: Outlook111A, Gmail111B, Word111C, and OneNote111D.

As summarized above, the recommendation field104can be dynamically updated based on signals received from the user.FIG. 1Dillustrates the dynamic nature of the recommendation field104. As the user provides updated signals, in this case as the user adds the word “email,” to the word “compose”, the recommendation is dynamically updated. As shown, the recommendation field104only shows contextually relevant recommendations given the updated phrase “compose email.” The recommendation field now only lists Outlook and Gmail. Thus, in some configurations, certain keywords can be used as a filter to remove applications from the recommendation field104.

The present example continues atFIG. 1E, where the recommendation is updated further based on the input signal and contextual information. As the user continues to add text to the text field103, e.g., the user adds a name to the phrase, “Compose email to Ben.” In this example, it is a given that Ben is a coworker thus, a corporate or enterprise email application such as Outlook is selected. Thus, certain applications, such as a Gmail application, can be categorized as an application that is used for personal contacts or personal use, while other applications, such as Outlook can be used for work contacts or for work purposes. Thus, in response to identifying a name that is identified as a coworker, as shown inFIG. 1E, the recommendation field104is updated to only show the Outlook application. In this example, if a name of a personal contact were to be included in the input signal, the Gmail application may be ranked higher and positioned to the left of the Outlook application.

The present example continues atFIG. 1F, where the user continues to add to the text field103. In this example, the user completes the input signal by adding “RE designs” and the phrase “compose email to Ben RE designs” is completed. As an input signal is received, the input signal is dynamically parsed and keywords are identified. As described above, keywords that are associated with one or more applications can change the recommendation, and other keywords can be used to populate data fields of the selected application. As described below, the additional text provided inFIG. 1Fis utilized to populate fields within the selected application.

In the present example, the user can add a task item to the task list by selecting any one of the recommended applications in the recommendation field104. In the example shown inFIG. 1F, the user selects the Outlook icon to initiate the generation of a task item.FIG. 1Gshows the task list102comprising the task item112generated in response to the selection of the outlook icon shown inFIG. 1F. Additional task items112can be added to the task list102by repeating the steps described above.

To complete a task item112listed in the task list102, a user can select the task item112. In response to the selection, as shown inFIG. 1H, the computing device executes the selected application. In the present example, Outlook is executed and a new email message user interface is displayed. As described above, selected keywords from the input signal and other supplemental data retrieved from one or more resources can be used to populate fields. For instance, an email address associated with the name included in the input signal can be retrieved from an address book, database, etc. Also in this example, certain keywords from the input signal, such as the word “designs” can be included in the subject field. Other data such as a greeting or other text can be included in the body of the email. Keywords of the input signal and other user activity can also be analyzed to determine a user's intent. For example, if a user has been working on a file related to a “design,” those files may be automatically attached to the email or referenced in the email body. In other embodiments, related files can also be displayed as a recommendation to the user along with an action, such as “attach related file,” etc.

FIG. 2AthroughFIG. 2Eillustrate another user scenario utilizing the techniques disclosed herein. As shown inFIG. 2A, an example input signal includes the word “compose.” In response to this input, the computing device displays a number of applications related to the entered keyword: Outlook, Gmail, Word, and OneNote. As shown inFIG. 2B, the user continues to enter text and provides an input “compose resume.” In response, the computing device displays all of the applications related to the entered phrase as it is being entered. The computing device dynamically analyzes the phrase to determine the associated applications, which in this example include the narrowing of the associated applications to Word and OneNote. In some configurations, applications can be re-ordered in this scenario. Thus, instead of having the Outlook and Gmail applications disappear from the recommendation, an updated list can re-order the applications so that the Word and OneNote applications are ranked as higher priority applications, e.g., they are positioned to indicate a higher ranking, and that the Gmail and Outlook applications are ranked as lower priority applications.

In this example, it is a given that the user selects the Word application. In response to receiving a user selection of the Word application inFIG. 2B, the computing device can update a task list102with a task item112as shown inFIG. 2C. As shown, the task item112can include an indication of the selected application and one or more aspects of the input signal.

As described above, when a task item112is generated, the computing device can generate task data that defines one or more actions that are used to complete a task.FIG. 2Dillustrates a portion of some example task data250that includes a list of actions251. As shown, the task data250includes a number of actions such as execute Word application, open resume template, retrieve user contact information, populate user contact information in the address header, etc. Such actions can be generated based on the input signal provided by a user, user history data, and/or contextual data retrieved from one or more resources. The task data250can be in any suitable format, which may include a script, code, macro, or any other instructions that define computer-implemented actions for completing a task. Thus, when a user selects a task item112from the task list102, a computing device may execute the actions defined in the task data250.

In the present example, when the user selects the task item112shown inFIG. 2C, the actions defined in the task data250can be executed. The execution of the task data250can result in the display of a Word file having pre-populated fields as shown inFIG. 2E.

FIG. 3AthroughFIG. 3Cillustrate another example scenario showing the dynamic nature of the recommendations. In this example, as shown inFIG. 3A, a user may provide an input signal indicating the word “ping.” Given that this word can be used in association with a number of applications, several applications may be recommended. In this example, in response to the input signal including a single word, “ping,” a number of applications suitable for performing a “ping” is displayed. In this example, the recommendation includes a ranked list of applications: Outlook, Gmail, a phone application, Skype, and a browser.

As the user continues to provide input to the computing device, the ranked list of applications dynamically changes based on the user's intention, which can be determined by an analysis of the input signal and other contextual data described herein. In the example ofFIG. 3B, when the phrase includes “Ping Cindy,” the recommendation field104only includes the phone application and Skype. Such an update can be based on a number of factors. For instance, historical user activity data can indicate that the user has used these applications to communicate with this particular contact, Cindy, in the past. The ranking of the applications can be based on a number of instances the user has utilized each application. For instance, in the example ofFIG. 3B, the phone application may be ranked higher than the Skype application because the phone application may have a higher number of uses and/or a higher number of occurrences with this particular contact, Cindy. Thus, the Skype application may be ranked higher for another contact. For example, in response to an input signal to “Ping Michael,” the task application may rank the Skype application above the phone application if the Skype application was used more than a threshold number of times compared to the phone application. A threshold and/or a comparison of a number of times an application is used can be processed by a machine learning engine for determining a confidence score for each application and/or a ranking of each application.

When the signal includes an object or a contact name that is associated with two different categories of applications, one or more factors derived from received contextual data can be used to determine a confidence score or a ranking of an application. For example, if the name Cindy is included in the input signal, and there happens to be a first Cindy in the user's family and a second Cindy at the user's office, one or more factors can be used to determine an appropriate application. In one specific example, a time of day or date can be used to determine a confidence score or a ranking of an application. For instance, if the user is creating the task during work hours, the task application may determine that the user intends to contact his or her co-worker. In this case, an Enterprise application such as Skype may be scored or ranked higher than a phone application. However, if the user is creating the task during evening hours, the task application may determine that the user intends to contact his or her family member. In this case, a personal application such as the phone application may be scored or ranked higher than an Enterprise application such as Skype. The same methods can be used for other contextual data, such as a day of the week, a location, etc. For instance, if the task is created while the user is at work, the Skype may be scored or ranked higher than a phone application, and if the task is created while the user is at home, the phone application may be scored or ranked higher than an Enterprise application such as Skype.

Each application may also be associated with a category, e.g., personal use, official use, etc. In addition, each contact name may be associated with at least one of the categories. Thus, when a contact name is included in an input signal, applications sharing the same category as the contact name may have a higher confidence score than other applications. At the same time, other factors, such as a time of day, a date, a day of the week, a location of the user, or contextual data describing any scenario related to the user, can be associated with a category. When an input signal indicates any one of these factors, applications sharing the same category as such factors may have a higher confidence score than other applications.

As shown inFIG. 3C, the computing device can generate a task item112to “Ping Cindy,” and the task item is displayed on a task list102. This task item may be generated in response to the user selection of a particular application listed in the recommendation field104, or the highest-ranking application may be automatically selected, which may occur after a predetermined period of time after the recommendation is displayed. In this example, the user has selected the Skype application shown in the recommendation field104. In response to the selection, the Skype application is associated with the task and displayed in association with the task item112.

The computing device can also generate task data that associates the task item112with the selected application, e.g., Skype. The computing device may also generate and process task data comprising a number of actions that can be used to perform the task defined in the task item112. For instance, task data can be used to cause a computing device to retrieve a phone number, an identifier, or any other information needed to make a call or generate a text message, and such information can be stored in association with the task item. Thus, when a user selects the task item112, the computing device can utilize the retrieved information to carry out the task. In this example, the task application may cause the execution of the Skype application and populate one or more fields within the Skype application to facilitate a call or to generate a message to a particular user. In this example, Cindy's contact information is retrieved from the phone book or another database, and the contact information may be stored within the task data.

FIG. 4AandFIG. 4Billustrate how a ranking of applications can change over time.FIG. 4Ashows a recommendation list having a rank list of applications. In this example, the applications may be ranked by a confidence score based on keywords in the input signal and/or other contextual data. As described above, a ranked list may change over time as user activity data is continually dynamically updated. Thus, after a user has made a threshold number of selections of an application thereby changing a confidence score of that application, the order in which that particular application is displayed relative to other applications in a rank list may change over time. Such embodiments can use a machine learning engine to store and analyze user history activity data to update confidence scores and determine when an order of application recommendations is to be updated.

In the present example, it is a given that the second application has been selected a threshold number of times. When the second application, the Gmail111B application, is selected a threshold number of times, based on user activity data generated by one or more machine learning engines, a confidence score associated with the second application may increase to a threshold level where the second application is listed as the highest priority, as shown inFIG. 4B.

The confidence score can be determined by the use of a number of different factors. For instance, the confidence score may be determined by an analysis of keywords in the phrase and/or user activity data (“historical data”). In some configurations, machine learning techniques may be utilized to analyze a user's activity history, e.g., which applications have been used in the past for a particular task. As the user interacts with the computer, user activity data is stored and analyzed by one or more machine learning techniques.

The term “machine learning” may refer to one or more programs that learns from the data it receives. For example, a machine learning mechanism may build, modify or otherwise utilize a model that is created from example inputs and makes predictions or decisions using the model. In the current example, the machine learning mechanism may be used to improve the identification of an application that is relevant to a user's intent and/or situation. Different factors can be used to influence the selection and/or the ranking of an application such as a number of times a user has utilized an application, how recently a user has utilized an application, etc. In addition, machine learning techniques can be utilized to determine a user's intent based on any conditional data referred to herein. Thus, activity and circumstances related to objects and/or people included in the phrase can be utilized to select and/or rank an application, e.g., determine a confidence score.

Different machine learning mechanisms may be utilized. For example, a classification mechanism may be utilized to determine a confidence score. The classification mechanism may classify the applications, keywords or phrases into different categories that provide an indication of whether a particular application is suitable for a task. A confidence score can be associated with each application. In some configurations, more classification categories may be utilized. In other examples, a statistical mechanism may be utilized to determine a confidence score. For example, a linear regression mechanism may be utilized to generate a confidence score that indicates a likelihood that an application is associated with a particular task. Linear regression may refer to a process for modeling the relationship between one variable with one or more other variables. Different linear regression models might be used to calculate the confidence score. For example, a least squares approach might be utilized, a maximum-likelihood estimation might be utilized, or another approach might be utilized.

For example, the operations of the routine500are described herein as being implemented, at least in part, by modules running the features disclosed herein and can be a dynamically linked library (DLL), a statically linked library, functionality produced by an application programming interface (API), a compiled program, an interpreted program, a script or any other executable set of instructions. Data can be stored in a data structure in one or more memory components. Data can be retrieved from the data structure by addressing links or references to the data structure.

Although the following illustration refers to the components of the figures, it can be appreciated that the operations of the routine500may be also implemented in many other ways. For example, the routine500may be implemented, at least in part, by a processor of another remote computer or a local circuit. In addition, one or more of the operations of the routine500may alternatively or additionally be implemented, at least in part, by a chipset working alone or in conjunction with other software modules. In the example described below, one or more modules of a computing system can receive and/or process the data disclosed herein. Any service, circuit or application suitable for providing the techniques disclosed herein can be used in operations described herein.

With reference toFIG. 5, the routine500begins at operation501where one or more modules of a computing system receive input signals from one or more devices. The input signals can include a voice input, a text input, or any other type of input that includes phrases indicating a task. Thus, a device capturing an input signal can include a keyboard, a camera, a microphone, or any other device that can capture a gesture of the user. In one illustrative example, a home device such as Google Home or an Amazon Echo can provide an input signal for processing. Such information can be documented at a server and communicated to a remote device such as a mobile phone, a tablet, or any other type of personal computer.

Next, at operation503, one or more modules of a computing system can parse the input signals to identify a phrase indicating a task. For example, one or more machine learning algorithms can be used to interpret the text of an input signal to identify a task. A phrase indicating a task may include a verb such as compose, send, set, etc. Technology can parse objects associated with the verb, such as a person's name. A phrase indicating a task can be, for example, “email Bob” or “call Steve,” etc.

At operation503, a computing system can also retrieve supplemental data related to the phrase. For instance, if a person's name is included in the phrase, the computing system may analyze a number of different databases, including a user's contact list, to obtain a person's last name, email address, address information, etc. The computing system may also retrieve other related data, such as calendar data, organizational data, etc. Such information can be used to determine additional context with respect to a person or object related to the phrase. For instance, organizational data or calendar data can be utilized to determine if a person is a work contact or a personal contact.

Next, at operation505, one or more modules of a computing system can select one or more applications that are suitable for performing the task. For instance, if the phrase includes the word “compose” or “send,” the computing system may select a number of applications such as word processing applications, email applications, no applications, etc. In some implementations, specific tasks may be associated with the category of applications. Each application in that category may be selected at operation505. Applications may also be selected by the use of contextual data that includes user history information. For example, if a user has provided the phrase “send message” as an input, and in the past the user has utilized a texting application, that texting application may be selected at operation505along with other applications such as email applications, web browsers, etc.

Next, at operation507, one or more modules of a computing device can determine a confidence score for individual applications. The confidence score can be determined by the use of a number of different factors. For instance, the confidence score may be determined by an analysis of keywords in the phrase and user activity data (“historical data”). One or more machine learning algorithms can also be utilized at operation507.

For instance, machine learning techniques may be utilized to analyze a user's activity history, e.g., which applications have been used in the past for a particular task. As the user interacts with the computer, user activity data is stored and analyzed by one or more machine learning techniques. The term “machine learning” may refer to one or more programs that learns from the data it receives. For example, a machine learning mechanism may build, modify or otherwise utilize a model that is created from example inputs and makes predictions or decisions using the model. In the current example, the machine learning mechanism may be used to improve the identification of an application that is relevant to a user's intent and/or situation. Different factors can be used to influence the selection and/or the ranking of an application such as a number of times a user has utilized an application, how recently a user has utilized an application, etc. In addition, machine learning techniques can be utilized to determine a user's intent based on any conditional data referred to herein. Thus, activity and circumstances related to objects and/or people included in the phrase can be utilized to select and/or rank an application, e.g., determine a confidence score.

Different machine learning mechanisms may be utilized. For example, a classification mechanism may be utilized to determine a confidence score. The classification mechanism may classify the applications, keywords or phrases into different categories that provide an indication of whether a particular application is suitable for a task. A confidence score can be associated with each application. In some configurations, more classification categories may be utilized. In other examples, a statistical mechanism may be utilized to determine a confidence score. For example, a linear regression mechanism may be utilized to generate a confidence score that indicates a likelihood that an application is associated with a particular task. Linear regression may refer to a process for modeling the relationship between one variable with one or more other variables. Different linear regression models might be used to calculate the confidence score. For example, a least squares approach might be utilized, a maximum-likelihood estimation might be utilized, or another approach might be utilized.

Next, at operation509, one or more modules of a computing device can display a ranked list of recommendations based on the confidence score of each application. One example of a ranked list is shown inFIG. 1C. In this example, the ranked list orders the application with the application having the highest confidence score arranged on the left side of the user interface and the other applications are ordered by the confidence score with the application having the lowest confidence score arranged on the far right side of the user interface. A confidence score can also be displayed with each application. In addition, some applications having a confidence score meeting a threshold can be displayed and other applications that do not meet the threshold are not displayed.

Next, at operation511, or more modules of a computing device can update a task list with a task item in response to a user selection of an application. One illustrative example is shown inFIG. 1F, where a user selects the Outlook icon and in response to such a selection, the task list shown inFIG. 1Gis updated with a new a task item112. The task item112can include a listing of the captured phrase and a graphical indicator of the selected application, e.g., Outlook.

At operation511, in response to a user selection of the task item112, the computing device may execute the selected application to perform the task. The computing device may also populate one or more data fields of the application to assist the user in completing a task. The fields may be populated with supplemental data or data gathered from the phrase provided in the input signal.

In one illustrative example, the routine500can include the operation of receiving input signals at a computing device. The routine500can also include parsing the input signals to identify a phrase indicating a task. A task can include any type of data manipulation or data processing that can be conducted by a computing device, such as sending an email, sending a message, generating a document, etc. The routine500can also include the operation of selecting a plurality of applications that are suitable for performing the task. The applications can include applications, scripts, services, Web-based services, local-services, scripts or any other code for processing data. The routine500can also include the operation of determining a confidence score for individual applications of the plurality of applications based on keywords in the phrase. A confidence score can be higher or lower depending on a number of factors, such as, a time period that has lapsed since a user has utilized a particular application, a time period that has lapsed since a user has utilized an application for contacting a particular user, a category of an application in comparison to a category of a keyword in the input signal, etc.

The routine can include generating a ranked list of the plurality of applications based on the confidence score for the individual applications and displaying the ranked list recommending the plurality of applications on a display screen in communication with the computing device, wherein an order of the individual applications within the ranked list is based on an associated confidence score.

In some configurations, a computer can receive a user selection of a selected application from the ranked list of applications. In some configurations, the selection of an application can be made by a computing device based on a comparison of a confidence score with a threshold. In some configurations, the selection of an application can include a highest-ranking application based on the confidence score. In response to the selection of the selected application, the routine can include updating a task list to include a task item providing a description of the task and an indication of the selected application, wherein a selection of the task list causes the execution of the selected application. In some configurations, the routine can include receiving a selection of the task item, and in other configurations, the selection can be automated. In response to the selection of the task item, the routine an include the execution of the selected application for purposes of completing the task. Fields of the application can be populated by data derived from the input signal or from contextual data or supplemental data. In some configurations, the routine can include an operation where a computer can activate the functionality of the selected application based on the individual actions of the task item. For instance, an action defined in a task item can direct the selected application to utilize the functionality of an application that opens a template document or template email. The functionality can include the use of any feature, e.g., a cut, paste, formatting functionality, opening of a specific window such as a new email draft, or any other application function that can be controlled by a macro, script, remote application, source code, a plug-in, add-in, etc.

FIG. 6shows additional details of an example computer architecture600for a computer, such as the computing device106(FIG. 1), capable of executing the program components described herein. Thus, the computer architecture600illustrated inFIG. 6illustrates an example architecture for a server computer, a mobile phone, a PDA, a smart phone, a desktop computer, a netbook computer, a tablet computer, and/or a laptop computer (not shown inFIG. 6). The computer architecture600may be utilized to execute any aspects of the software components presented herein.

The computer architecture600illustrated inFIG. 6includes a central processing unit602(“CPU”), a system memory604, including a random access memory606(“RAM”) and a read-only memory (“ROM”)608, and a system bus610that couples the memory604to the CPU602. A basic input/output system containing the basic routines that help to transfer information between elements within the computer architecture600, such as during startup, is stored in the ROM608. The computer architecture600further includes a mass storage device612for storing an operating system607, other data, and one or more application programs, such as a task application141.

The mass storage device612is connected to the CPU602through a mass storage controller (not shown inFIG. 6) connected to the bus610. The mass storage device612and its associated computer-readable media provide non-volatile storage for the computer architecture600. Although the description of computer-readable media contained herein refers to a mass storage device, such as a solid-state drive, a hard disk or CD-ROM drive, it should be appreciated by those skilled in the art that computer-readable media can be any available computer storage media or communication media that can be accessed by the computer architecture600.

According to various configurations, the computer architecture600may operate in a networked environment using logical connections to remote computers through the network656and/or another network (not shown). The computer architecture600may connect to the network656through a network interface unit614connected to the bus610. It should be appreciated that the network interface unit614also may be utilized to connect to other types of networks and remote computer systems. The computer architecture600also may include an input/output controller616for receiving and processing input from a number of other devices, including a keyboard, mouse, or electronic stylus (not shown inFIG. 6). Similarly, the input/output controller616may provide output to a display screen, a printer, or other type of output device (also not shown inFIG. 6).

It should be appreciated that the software components described herein may, when loaded into the CPU602and executed, transform the CPU602and the overall computer architecture600from a general-purpose computing system into a special-purpose computing system customized to facilitate the functionality presented herein. The CPU602may be constructed from any number of transistors or other discrete circuit elements, which may individually or collectively assume any number of states. More specifically, the CPU602may operate as a finite-state machine, in response to executable instructions contained within the software modules disclosed herein. These computer-executable instructions may transform the CPU602by specifying how the CPU602transitions between states, thereby transforming the transistors or other discrete hardware elements constituting the CPU602.

In light of the above, it should be appreciated that many types of physical transformations take place in the computer architecture600in order to store and execute the software components presented herein. It also should be appreciated that the computer architecture600may include other types of computing devices, including hand-held computers, embedded computer systems, personal digital assistants, and other types of computing devices known to those skilled in the art. It is also contemplated that the computer architecture600may not include all of the components shown inFIG. 6, may include other components that are not explicitly shown inFIG. 6, or may utilize an architecture completely different than that shown inFIG. 6.

FIG. 7depicts an illustrative distributed computing environment700capable of executing the software components described herein. Thus, the distributed computing environment700illustrated inFIG. 7can be utilized to execute any aspects of the software components presented herein. For example, the distributed computing environment700can be utilized to execute aspects of the software components described herein.

According to various implementations, the distributed computing environment700includes a computing environment702operating on, in communication with, or as part of the network704. The network704may be or may include the network656, described above with reference toFIG. 6. The network704also can include various access networks. One or more client devices706A-706N (hereinafter referred to collectively and/or generically as “clients706” and also referred to herein as computing devices, which generate the user interfaces depicted herein) can communicate with the computing environment702via the network704and/or other connections (not illustrated inFIG. 7). In one illustrated configuration, the clients706include a computing device706A such as a laptop computer, a desktop computer, or other computing device; a slate or tablet computing device (“tablet computing device”)706B; a mobile computing device706C such as a mobile telephone, a smart phone, or other mobile computing device; a server computer706D; and/or other devices706N. It should be understood that any number of clients706can communicate with the computing environment702. Two example computing architectures for the clients706are illustrated and described herein with reference toFIGS. 5 and 7. It should be understood that the illustrated clients706and computing architectures illustrated and described herein are illustrative, and should not be construed as being limited in any way. Each client706can be used to collect input signals and share them between different clients.

In the illustrated configuration, the computing environment702includes application servers708, data storage710, and one or more network interfaces712. According to various implementations, the functionality of the application servers708can be provided by one or more server computers that are executing as part of, or in communication with, the network704. The application servers708can host various services, virtual machines, portals, and/or other resources. In the illustrated configuration, the application servers708host one or more virtual machines714for hosting applications or other functionality. According to various implementations, the virtual machines714host one or more applications and/or software modules for enabling the generation of content from productivity applications. It should be understood that this configuration is illustrative, and should not be construed as being limiting in any way. The application servers708also host or provide access to one or more portals, link pages, Web sites, and/or other information (“Web portals”)716.

According to various implementations, the application servers708also include one or more mailbox services718and one or more messaging services720. The mailbox services718can include electronic mail (“email”) services. The mailbox services718also can include various personal information management (“PIM”) and presence services including, but not limited to, calendar services, contact management services, collaboration services, and/or other services. The messaging services720can include, but are not limited to, instant messaging services, chat services, forum services, and/or other communication services.

The application servers708also may include one or more social networking services722. The social networking services722can include various social networking services including, but not limited to, services for sharing or posting status updates, instant messages, links, photos, videos, and/or other information; services for commenting or displaying interest in articles, products, blogs, or other resources; and/or other services. In some configurations, the social networking services722are provided by or include the FACEBOOK social networking service, the LINKEDIN professional networking service, the MYSPACE social networking service, the FOURSQUARE geographic networking service, the YAMMER office colleague networking service, and the like. In other configurations, the social networking services722are provided by other services, sites, and/or providers that may or may not be explicitly known as social networking providers. For example, some web sites allow users to interact with one another via email, chat services, and/or other means during various activities and/or contexts such as reading published articles, commenting on goods or services, publishing, collaboration, gaming, and the like. Examples of such services include, but are not limited to, the WINDOWS LIVE service and the XBOX LIVE service from Microsoft Corporation in Redmond, Wash. Other services are possible and are contemplated.

The social networking services722also can include commenting, blogging, and/or micro blogging services. Examples of such services include, but are not limited to, the YELP commenting service, the KUDZU review service, the OFFICETALK enterprise micro blogging service, the TWITTER messaging service, the GOOGLE BUZZ service, and/or other services. It should be appreciated that the above lists of services are not exhaustive and that numerous additional and/or alternative social networking services722are not mentioned herein for the sake of brevity. As such, the above configurations are illustrative, and should not be construed as being limited in any way. According to various implementations, the social networking services722may host one or more applications and/or software modules for providing the functionality described herein, such as enabling the generation of content from multiple applications. For instance, any one of the application servers708may communicate or facilitate the functionality and features described herein. For instance, a social networking application, mail client, messaging client or a browser running on a phone or any other client706may communicate with a networking service722and facilitate the functionality, even in part, described above with respect toFIG. 7. Any device or service depicted herein can be used as a resource for supplemental data, including email servers, storage servers, etc.

As shown inFIG. 7, the application servers708also can host other services, applications, portals, and/or other resources (“other resources”)724. The other resources724can include, but are not limited to, document sharing, rendering or any other functionality. The other resources724can also include a machine learning engine for processing and generating historical user activity data. It thus can be appreciated that the computing environment702can provide integration of the concepts and technologies disclosed herein with various mailbox, messaging, social networking, and/or other services or resources.

As mentioned above, the computing environment702can include the data storage710. According to various implementations, the functionality of the data storage710is provided by one or more databases operating on, or in communication with, the network704. The functionality of the data storage710also can be provided by one or more server computers configured to host data for the computing environment702. The data storage710can include, host, or provide one or more real or virtual datastores726A-726N (hereinafter referred to collectively and/or generically as “datastores726”). The datastores726are configured to host data used or created by the application servers708and/or other data. Although not illustrated inFIG. 7, the datastores726also can host or store web page documents, Word documents, presentation documents, data structures, algorithms for execution by a recommendation engine, and/or other data utilized by any application program or another module. Aspects of the datastores726may be associated with a service for storing files.

The computing environment702can communicate with, or be accessed by, the network interfaces712. The network interfaces712can include various types of network hardware and software for supporting communications between two or more computing devices including, but not limited to, the computing devices and the servers. It should be appreciated that the network interfaces712also may be utilized to connect to other types of networks and/or computer systems.

It should be understood that the distributed computing environment700described herein can provide any aspects of the software elements described herein with any number of virtual computing resources and/or other distributed computing functionality that can be configured to execute any aspects of the software components disclosed herein. According to various implementations of the concepts and technologies disclosed herein, the distributed computing environment700provides the software functionality described herein as a service to the computing devices. It should be understood that the computing devices can include real or virtual machines including, but not limited to, server computers, web servers, personal computers, mobile computing devices, smart phones, and/or other devices. As such, various configurations of the concepts and technologies disclosed herein enable any device configured to access the distributed computing environment700to utilize the functionality described herein for providing the techniques disclosed herein, among other aspects. In one specific example, as summarized above, techniques described herein may be implemented, at least in part, by a web browser application, which works in conjunction with the application servers708ofFIG. 7.

Turning now toFIG. 8, an illustrative computing device architecture800for a computing device that is capable of executing various software components is described herein for enabling the generation of content from multiple applications. The computing device architecture800is applicable to computing devices that facilitate mobile computing due, in part, to form factor, wireless connectivity, and/or battery-powered operation. In some configurations, the computing devices include, but are not limited to, mobile telephones, tablet devices, slate devices, portable video game devices, and the like. The computing device architecture800is applicable to any of the computing devices shown inFIG. 1andFIG. 7. Moreover, aspects of the computing device architecture800may be applicable to traditional desktop computers, portable computers (e.g., phones, laptops, notebooks, ultra-portables, and netbooks), server computers, and other computer systems, such as those described herein with reference toFIG. 1andFIG. 7. For example, the single touch and multi-touch aspects disclosed herein below may be applied to desktop computers that utilize a touchscreen or some other touch-enabled device, such as a touch-enabled track pad or touch-enabled mouse.

The computing device architecture800illustrated inFIG. 8includes a processor802, memory components804, network connectivity components806, sensor components808, input/output components810, and power components812. In the illustrated configuration, the processor802is in communication with the memory components804, the network connectivity components806, the sensor components808, the input/output (“I/O”) components810, and the power components812. Although no connections are shown between the individual components illustrated inFIG. 8, the components can interact to carry out device functions. In some configurations, the components are arranged so as to communicate via one or more busses (not shown).

The processor802includes a central processing unit (“CPU”) configured to process data, execute computer-executable instructions of one or more application programs, and communicate with other components of the computing device architecture800in order to perform various functionality described herein. The processor802may be utilized to execute aspects of the software components presented herein and, particularly, those that utilize, at least in part, a touch-enabled input.

In some configurations, the processor802includes a graphics processing unit (“GPU”) configured to accelerate operations performed by the CPU, including, but not limited to, operations performed by executing general-purpose scientific and/or engineering computing applications, as well as graphics-intensive computing applications such as high resolution video (e.g., 720P, 1080P, and higher resolution), video games, three-dimensional (“3D”) modeling applications, and the like. In some configurations, the processor802is configured to communicate with a discrete GPU (not shown inFIG. 8). In any case, the CPU and GPU may be configured in accordance with a co-processing CPU/GPU computing model, wherein the sequential part of an application executes on the CPU and the computationally-intensive part is accelerated by the GPU.

In some configurations, the processor802is, or is included in, a system-on-chip (“SoC”) along with one or more of the other components described herein below. For example, the SoC may include the processor802, a GPU, one or more of the network connectivity components806, and one or more of the sensor components808. In some configurations, the processor802is fabricated, in part, utilizing a package-on-package (“PoP”) integrated circuit packaging technique. The processor802may be a single core or multi-core processor.

The processor802may be created in accordance with an ARM architecture, available for license from ARM HOLDINGS of Cambridge, United Kingdom. Alternatively, the processor802may be created in accordance with an x86 architecture, such as is available from INTEL CORPORATION of Mountain View, Calif. and others. In some configurations, the processor802is a SNAPDRAGON SoC, available from QUALCOMM of San Diego, Calif., a TEGRA SoC, available from NVIDIA of Santa Clara, Calif., a HUMMINGBIRD SoC, available from SAMSUNG of Seoul, South Korea, an Open Multimedia Application Platform (“OMAP”) SoC, available from TEXAS INSTRUMENTS of Dallas, Tex., a customized version of any of the above SoCs, or a proprietary SoC.

The memory components804include a random access memory (“RAM”)814, a read-only memory (“ROM”)816, an integrated storage memory (“integrated storage”)818, and a removable storage memory (“removable storage”)820. In some configurations, the RAM814or a portion thereof, the ROM816or a portion thereof, and/or some combination of the RAM814and the ROM816is integrated in the processor802. In some configurations, the ROM816is configured to store a firmware, an operating system or a portion thereof (e.g., operating system kernel), and/or a bootloader to load an operating system kernel from the integrated storage818and/or the removable storage820.

The integrated storage818can include a solid-state memory, a hard disk, or a combination of solid-state memory and a hard disk. The integrated storage818may be soldered or otherwise connected to a logic board upon which the processor802and other components described herein also may be connected. As such, the integrated storage818is integrated in the computing device. The integrated storage818is configured to store an operating system or portions thereof, application programs, data, and other software components described herein.

The removable storage820can include a solid-state memory, a hard disk, or a combination of solid-state memory and a hard disk. In some configurations, the removable storage820is provided in lieu of the integrated storage818. In other configurations, the removable storage820is provided as additional optional storage. In some configurations, the removable storage820is logically combined with the integrated storage818such that the total available storage is made available as a total combined storage capacity. In some configurations, the total combined capacity of the integrated storage818and the removable storage820is shown to a user instead of separate storage capacities for the integrated storage818and the removable storage820.

The removable storage820is configured to be inserted into a removable storage memory slot (not shown onFIG. 8) or other mechanism by which the removable storage820is inserted and secured to facilitate a connection over which the removable storage820can communicate with other components of the computing device, such as the processor802. The removable storage820may be embodied in various memory card formats including, but not limited to, PC card, CompactFlash card, memory stick, secure digital (“SD”), miniSD, microSD, universal integrated circuit card (“UICC”) (e.g., a subscriber identity module (“SIM”) or universal SIM (“USIM”)), a proprietary format, or the like.

It can be understood that one or more of the memory components804can store an operating system. According to various configurations, the operating system includes, but is not limited to WINDOWS MOBILE OS from Microsoft Corporation of Redmond, Wash., WINDOWS PHONE OS from Microsoft Corporation, WINDOWS from Microsoft Corporation, PALM WEB OS from Hewlett-Packard Company of Palo Alto, Calif., BLACKBERRY OS from Research In Motion Limited of Waterloo, Ontario, Canada, IOS from Apple Inc. of Cupertino, Calif., and ANDROID OS from Google Inc. of Mountain View, Calif. Other operating systems are contemplated.

The network connectivity components806include a wireless wide area network component (“WWAN component”)822, a wireless local area network component (“WLAN component”)824, and a wireless personal area network component (“WPAN component”)826. The network connectivity components806facilitate communications to and from the network856or another network, which may be a WWAN, a WLAN, or a WPAN. Although only the network856is illustrated, the network connectivity components806may facilitate simultaneous communication with multiple networks, including the network656ofFIG. 6. For example, the network connectivity components806may facilitate simultaneous communications with multiple networks via one or more of a WWAN, a WLAN, or a WPAN.

The network856may be or may include a WWAN, such as a mobile telecommunications network utilizing one or more mobile telecommunications technologies to provide voice and/or data services to a computing device utilizing the computing device architecture800via the WWAN component822. The mobile telecommunications technologies can include, but are not limited to, Global System for Mobile communications (“GSM”), Code Division Multiple Access (“CDMA”) ONE, CDMA7000, Universal Mobile Telecommunications System (“UMTS”), Long Term Evolution (“LTE”), and Worldwide Interoperability for Microwave Access (“WiMAX”). Moreover, the network856may utilize various channel access methods (which may or may not be used by the aforementioned standards) including, but not limited to, Time Division Multiple Access (“TDMA”), Frequency Division Multiple Access (“FDMA”), CDMA, wideband CDMA (“W-CDMA”), Orthogonal Frequency Division Multiplexing (“OFDM”), Space Division Multiple Access (“SDMA”), and the like. Data communications may be provided using General Packet Radio Service (“GPRS”), Enhanced Data rates for Global Evolution (“EDGE”), the High-Speed Packet Access (“HSPA”) protocol family including High-Speed Downlink Packet Access (“HSDPA”), Enhanced Uplink (“EUL”) or otherwise termed High-Speed Uplink Packet Access (“HSUPA”), Evolved HSPA (“HSPA+”), LTE, and various other current and future wireless data access standards. The network856may be configured to provide voice and/or data communications with any combination of the above technologies. The network856may be configured to or adapted to provide voice and/or data communications in accordance with future generation technologies.

In some configurations, the WWAN component822is configured to provide dual-multi-mode connectivity to the network856. For example, the WWAN component822may be configured to provide connectivity to the network856, wherein the network856provides service via GSM and UMTS technologies, or via some other combination of technologies. Alternatively, multiple WWAN components822may be utilized to perform such functionality, and/or provide additional functionality to support other non-compatible technologies (i.e., incapable of being supported by a single WWAN component). The WWAN component822may facilitate similar connectivity to multiple networks (e.g., a UMTS network and an LTE network).

The network856may be a WLAN operating in accordance with one or more Institute of Electrical and Electronic Engineers (“IEEE”) 802.11 standards, such as IEEE 802.11a, 802.11b, 802.11g, 802.11n, and/or future 802.11 standards (referred to herein collectively as WI-FI). Draft 802.11 standards are also contemplated. In some configurations, the WLAN is implemented utilizing one or more wireless WI-FI access points. In some configurations, one or more of the wireless WI-FI access points are another computing device with connectivity to a WWAN that are functioning as a WI-FI hotspot. The WLAN component824is configured to connect to the network856via the WI-FI access points. Such connections may be secured via various encryption technologies including, but not limited, WI-FI Protected Access (“WPA”), WPA2, Wired Equivalent Privacy (“WEP”), and the like.

The network856may be a WPAN operating in accordance with Infrared Data Association (“IrDA”), BLUETOOTH, wireless Universal Serial Bus (“USB”), Z-Wave, ZIGBEE, or some other short-range wireless technology. In some configurations, the WPAN component826is configured to facilitate communications with other devices, such as peripherals, computers, or other computing devices via the WPAN.

The sensor components808include a magnetometer828, an ambient light sensor830, a proximity sensor832, an accelerometer834, a gyroscope836, and a Global Positioning System sensor (“GPS sensor”)838. It is contemplated that other sensors, such as, but not limited to, temperature sensors or shock detection sensors, also may be incorporated in the computing device architecture800.

The magnetometer828is configured to measure the strength and direction of a magnetic field. In some configurations the magnetometer828provides measurements to a compass application program stored within one of the memory components804in order to provide a user with accurate directions in a frame of reference including the cardinal directions, north, south, east, and west. Similar measurements may be provided to a navigation application program that includes a compass component. Other uses of measurements obtained by the magnetometer828are contemplated.

The ambient light sensor830is configured to measure ambient light. In some configurations, the ambient light sensor830provides measurements to an application program stored within one the memory components804in order to automatically adjust the brightness of a display (described below) to compensate for low-light and high-light environments. Other uses of measurements obtained by the ambient light sensor830are contemplated.

The proximity sensor832is configured to detect the presence of an object or thing in proximity to the computing device without direct contact. In some configurations, the proximity sensor832detects the presence of a user's body (e.g., the user's face) and provides this information to an application program stored within one of the memory components804that utilizes the proximity information to enable or disable some functionality of the computing device. For example, a telephone application program may automatically disable a touchscreen (described below) in response to receiving the proximity information so that the user's face does not inadvertently end a call or enable/disable other functionality within the telephone application program during the call. Other uses of proximity as detected by the proximity sensor832are contemplated.

The accelerometer834is configured to measure proper acceleration. In some configurations, output from the accelerometer834is used by an application program as an input mechanism to control some functionality of the application program. For example, the application program may be a video game in which a character, a portion thereof, or an object is moved or otherwise manipulated in response to input received via the accelerometer834. In some configurations, output from the accelerometer834is provided to an application program for use in switching between landscape and portrait modes, calculating coordinate acceleration, or detecting a fall. Other uses of the accelerometer834are contemplated.

The gyroscope836is configured to measure and maintain orientation. In some configurations, output from the gyroscope836is used by an application program as an input mechanism to control some functionality of the application program. For example, the gyroscope836can be used for accurate recognition of movement within a 3D environment of a video game application or some other application. In some configurations, an application program utilizes output from the gyroscope836and the accelerometer834to enhance control of some functionality of the application program. Other uses of the gyroscope836are contemplated.

The GPS sensor838is configured to receive signals from GPS satellites for use in calculating a location. The location calculated by the GPS sensor838may be used by any application program that requires or benefits from location information. For example, the location calculated by the GPS sensor838may be used with a navigation application program to provide directions from the location to a destination or directions from the destination to the location. Moreover, the GPS sensor838may be used to provide location information to an external location-based service, such as E911 service. The GPS sensor838may obtain location information generated via WI-FI, WIMAX, and/or cellular triangulation techniques utilizing one or more of the network connectivity components806to aid the GPS sensor838in obtaining a location fix. The GPS sensor838may also be used in Assisted GPS (“A-GPS”) systems. The GPS sensor838can also operate in conjunction with other components, such as the processor802, to generate positioning data for the computing device800.

The I/O components810include a display840, a touchscreen842, a data I/O interface component (“data I/O”)844, an audio I/O interface component (“audio I/O”)846, a video I/O interface component (“video I/O”)848, and a camera850. In some configurations, the display840and the touchscreen842are combined. In some configurations two or more of the data I/O component844, the audio I/O component846, and the video I/O component848are combined. The I/O components810may include discrete processors configured to support the various interfaces described below, or may include processing functionality built-in to the processor802.

The display840is an output device configured to present information in a visual form. In particular, the display840may present graphical user interface (“GUI”) elements, text, images, video, notifications, virtual buttons, virtual keyboards, messaging data, Internet content, device status, time, date, calendar data, preferences, map information, location information, and any other information that is capable of being presented in a visual form. In some configurations, the display840is a liquid crystal display (“LCD”) utilizing any active or passive matrix technology and any backlighting technology (if used). In some configurations, the display840is an organic light emitting diode (“OLED”) display. Other display types are contemplated.

The touchscreen842, also referred to herein as a “touch-enabled screen,” is an input device configured to detect the presence and location of a touch. The touchscreen842may be a resistive touchscreen, a capacitive touchscreen, a surface acoustic wave touchscreen, an infrared touchscreen, an optical imaging touchscreen, a dispersive signal touchscreen, an acoustic pulse recognition touchscreen, or may utilize any other touchscreen technology. In some configurations, the touchscreen842is incorporated on top of the display840as a transparent layer to enable a user to use one or more touches to interact with objects or other information presented on the display840. In other configurations, the touchscreen842is a touch pad incorporated on a surface of the computing device that does not include the display840. For example, the computing device may have a touchscreen incorporated on top of the display840and a touch pad on a surface opposite the display840.

In some configurations, the touchscreen842is a single-touch touchscreen. In other configurations, the touchscreen842is a multi-touch touchscreen. In some configurations, the touchscreen842is configured to detect discrete touches, single touch gestures, and/or multi-touch gestures. These are collectively referred to herein as gestures for convenience. Several gestures will now be described. It should be understood that these gestures are illustrative and are not intended to limit the scope of the appended claims. Moreover, the described gestures, additional gestures, and/or alternative gestures may be implemented in software for use with the touchscreen842. As such, a developer may create gestures that are specific to a particular application program.

In some configurations, the touchscreen842supports a tap gesture in which a user taps the touchscreen842once on an item presented on the display840. The tap gesture may be used for various reasons including, but not limited to, opening or launching whatever the user taps. In some configurations, the touchscreen842supports a double tap gesture in which a user taps the touchscreen842twice on an item presented on the display840. The double tap gesture may be used for various reasons including, but not limited to, zooming in or zooming out in stages. In some configurations, the touchscreen842supports a tap and hold gesture in which a user taps the touchscreen842and maintains contact for at least a pre-defined time. The tap and hold gesture may be used for various reasons including, but not limited to, opening a context-specific menu.

In some configurations, the touchscreen842supports a pan gesture in which a user places a finger on the touchscreen842and maintains contact with the touchscreen842while moving the finger on the touchscreen842. The pan gesture may be used for various reasons including, but not limited to, moving through screens, images, or menus at a controlled rate. Multiple finger pan gestures are also contemplated. In some configurations, the touchscreen842supports a flick gesture in which a user swipes a finger in the direction the user wants the screen to move. The flick gesture may be used for various reasons including, but not limited to, scrolling horizontally or vertically through menus or pages. In some configurations, the touchscreen842supports a pinch and stretch gesture in which a user makes a pinching motion with two fingers (e.g., thumb and forefinger) on the touchscreen842or moves the two fingers apart. The pinch and stretch gesture may be used for various reasons including, but not limited to, zooming gradually in or out of a web site, map, or picture.

Although the above gestures have been described with reference to the use of one or more fingers for performing the gestures, other appendages such as toes or objects such as styluses may be used to interact with the touchscreen842. As such, the above gestures should be understood as being illustrative and should not be construed as being limiting in any way.

The data I/O interface component844is configured to facilitate input of data to the computing device and output of data from the computing device. In some configurations, the data I/O interface component844includes a connector configured to provide wired connectivity between the computing device and a computer system, for example, for synchronization operation purposes. The connector may be a proprietary connector or a standardized connector such as USB, micro-USB, mini-USB, or the like. In some configurations, the connector is a dock connector for docking the computing device with another device such as a docking station, audio device (e.g., a digital music player), or video device.

The audio I/O interface component846is configured to provide audio input and/or output capabilities to the computing device. In some configurations, the audio I/O interface component846includes a microphone configured to collect audio signals. In some configurations, the audio I/O interface component846includes a headphone jack configured to provide connectivity for headphones or other external speakers. In some configurations, the audio I/O interface component846includes a speaker for the output of audio signals. In some configurations, the audio I/O interface component846includes an optical audio cable out.

The video I/O interface component848is configured to provide video input and/or output capabilities to the computing device. In some configurations, the video I/O interface component848includes a video connector configured to receive video as input from another device (e.g., a video media player such as a DVD or BLURAY player) or send video as output to another device (e.g., a monitor, a television, or some other external display). In some configurations, the video I/O interface component848includes a High-Definition Multimedia Interface (“HDMI”), mini-HDMI, micro-HDMI, DisplayPort, or proprietary connector to input/output video content. In some configurations, the video I/O interface component848or portions thereof is combined with the audio I/O interface component846or portions thereof.

The camera850can be configured to capture still images and/or video. The camera850may utilize a charge coupled device (“CCD”) or a complementary metal oxide semiconductor (“CMOS”) image sensor to capture images. In some configurations, the camera850includes a flash to aid in taking pictures in low-light environments. Settings for the camera850may be implemented as hardware or software buttons.

Although not illustrated, one or more hardware buttons may also be included in the computing device architecture800. The hardware buttons may be used for controlling some operational aspect of the computing device. The hardware buttons may be dedicated buttons or multi-use buttons. The hardware buttons may be mechanical or sensor-based.

The illustrated power components812include one or more batteries852, which can be connected to a battery gauge854. The batteries852may be rechargeable or disposable. Rechargeable battery types include, but are not limited to, lithium polymer, lithium ion, nickel cadmium, and nickel metal hydride. Each of the batteries852may be made of one or more cells.

The battery gauge854can be configured to measure battery parameters such as current, voltage, and temperature. In some configurations, the battery gauge854is configured to measure the effect of a battery's discharge rate, temperature, age and other factors to predict remaining life within a certain percentage of error. In some configurations, the battery gauge854provides measurements to an application program that is configured to utilize the measurements to present useful power management data to a user. Power management data may include one or more of a percentage of battery used, a percentage of battery remaining, a battery condition, a remaining time, a remaining capacity (e.g., in watt hours), a current draw, and a voltage.

The power components812may also include a power connector (not shown inFIG. 8), which may be combined with one or more of the aforementioned I/O components810. The power components812may interface with an external power system or charging equipment via an I/O component.