US20180189521A1

US20180189521A1 - Analyzing data to determine an upload account

Info

Publication number: US20180189521A1
Application number: US15/600,619
Authority: US
Inventors: Rayyan Jaber; Daniel William CREVIER; Chia-Jiun Tan
Original assignee: Microsoft Technology Licensing LLC
Current assignee: Microsoft Technology Licensing LLC
Priority date: 2017-01-05
Filing date: 2017-05-19
Publication date: 2018-07-05
Also published as: CN110168592A; EP3566196A1; WO2018128954A1; CN110168588A; US20180189404A1; EP3566195A1; WO2018128953A1; CN110168588B; US10795952B2

Abstract

Technologies are described herein for analyzing data to determine an upload account. In some configurations, techniques disclosed herein cause the data, and other data, to be analyzed to determine whether the data is to be uploaded to a business account associated with the user or a personal account associated with the user. A request to upload data is received from a computing device. Instead of prompting a user to manually select whether to upload the data to the personal account or the business account, the techniques determine whether the data is personal or business related. When the data is determined to be personal, the data is uploaded to the personal account. When the data is determined to be business related, the data is uploaded to the business account.

Description

CROSS REFERENCE TO RELATED CASES

This Application claims the benefit of U.S. Patent Application No. 62/442,902, filed on Jan. 5, 2017, U.S. Patent Application No. 62/442,911, filed on Jan. 5, 2017, and U.S. Patent Application No. 62/442,915, filed on Jan. 5, 2017 which are hereby incorporated by reference in their entirety.

BACKGROUND

In recent years, it has become more common for people to access and store files using remote storage services. For example, a person may use remote storage services to store and access files from home, work, or school. Not only may an individual have a personal account with a remote storage service, the individual may also have a business account with the remote storage service. Although these services enable users to access and store data from multiple locations, it can be difficult or time consuming for a user to manually select the appropriate account. For example, when a person is signed into both a business account and a personal account of a storage service, the person may have to manually select the account to which data is to be uploaded. When using this practice, the user is required to confirm and/or change the account when uploading data.
It is with respect to these and other considerations that the disclosure made herein is presented.

SUMMARY

Technologies are described herein for analyzing data to determine an upload account. In some configurations, techniques disclosed herein cause data to be analyzed to determine whether the data is to be uploaded to a first account associated with the user (e.g., a business account) or a second account associated with the user (e.g., a personal account). Some illustrative configurations involve receiving a request to upload data from a computing device associated with the user. For instance, the data to upload may be a photograph taken by the computing device, or some other type of data, such as a word-processing document, a spreadsheet, a slide deck, and the like.
Instead of prompting the user to manually select whether to upload the data, such as a photograph, to the personal account or the business account, the techniques described herein determine whether the data is related to a business of the user, or whether the data is personal. According to some configurations, the system can use information identified from the data, as well as other data associated with the user when determining the account to select. For instance, when the data to upload is a photograph, the system can identify from an analysis of the photograph, or metadata of the photograph, information such as but not limited to the time of day and the date the photograph was taken; the people (e.g., family, friends, co-workers) and/or objects recognized in the photograph (e.g., a cake, a car, . . . ); the location of where the photograph was taken; text extracted from the photograph; the type of business the user is involved in, and the like.
As briefly mentioned, the system can also use data obtained from other sources, such as one or more of external data sources to determine whether the data is related to business or is personal. For instance, the system can access and analyze data from calendar programs (e.g., is the person in a business meeting or a personal meeting when the data was created?), organization charts (e.g., who are other people in the business, what level are they, how are they related?), contact lists (e.g., personal contact list and/or business contact list), social networks, document management platforms, and the like to provide more indications of whether the data is personal or business related.
As an example, if the data to upload is a photograph of a car, and the user is employed by an insurance company as a claims representative (e.g., as determined from user data, an organizational chart, or some other source), the system can determine that the photograph is business related. Similarly, if the photograph included a child of the user and the calendar of user indicated that the user was at a birthday party for the child, the system can determine that the photograph is personal. According to some techniques, the system can use on or more machine learning mechanisms to assist in determining whether to upload the data to a business account or a personal account.
When the data to upload is determined to be personal, the data is uploaded to the personal account of the user. When the data is determined to be business related, the data is uploaded to the business account of the user. The data to upload can be many types of data, such as but not limited to photographs, word-processing documents, audio recordings, videos, business files, and the like.
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. These and various other features will be apparent from a reading of the following Detailed Description and a review of the associated drawings.
This Summary is provided to introduce a selection of concepts in a simplified form that are further described below in the Detailed Description. This Summary is not intended to identify key features or essential features of the claimed subject matter, nor is it intended that this Summary be used to limit the scope of the claimed subject matter. Furthermore, the claimed subject matter is not limited to implementations that solve any or all disadvantages noted in any part of this disclosure.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram showing several example components of a system for analyzing data to determine an upload account.

FIG. 2 is a block diagram showing several example components of a server program module for analyzing data to determine an upload account.

FIG. 3 is a flow diagram illustrating aspects of a method for analyzing data to determine an upload account.

FIG. 4 is a computer architecture diagram illustrating an illustrative computer hardware and software architecture for a computing system capable of implementing aspects of the techniques and technologies presented herein.

FIG. 5 is a diagram illustrating a distributed computing environment capable of implementing aspects of the techniques and technologies presented herein.

FIG. 6 is a computer architecture diagram illustrating a computing device architecture for a computing device capable of implementing aspects of the techniques and technologies presented herein.

DETAILED DESCRIPTION

The following detailed description is directed to concepts and technologies for analyzing data to determine an upload account. In some configurations, techniques disclosed herein cause data to be analyzed to determine whether the data is to be uploaded to a first account, a second account, or some other account associated with the user. In some examples, the accounts include at least one personal account and at least one business account. The data to upload may be a photograph taken by the computing device, or some other type of data associated with the user.
Instead of prompting the user to make a manual selection of the account to upload the data to, the system analyzes the data to determine whether the data is more related to a first account or a second account of the user. For instance, the system can analyze the contents of the data, as well as other data, to determine information about the data. As briefly discussed above, the system use information identified from the data, as well as information obtained from other data sources. For instance, the system can identify from an analysis of the data, or metadata, information such as but not limited to the time of day and the date the data was created; one or more subjects of the data; the people (e.g., family, friends, co-workers) and/or objects identified from the data; the location of where the data was created; text extracted from the data; the type of business the user is involved in, and the like. As mentioned, the system can also use data obtained from other sources, such as one or more of external data sources to determine whether the data is related to business or is personal. For instance, the system can access and analyze data from calendar programs (e.g., is the person in a business meeting or a personal meeting when the data was created?), organization charts (e.g., who are other people in the business, what level are they, how are they related?), contact lists (e.g., personal contact list and/or business contact list), social networks, document management platforms, and the like to provide more indications of whether the data is personal or business related.
When the data is determined to be personal, the data is uploaded to the personal account. When the data is determined to be business related, the data is uploaded to the business account. The data to upload can be many types of data, such as but not limited to photographs, word-processing documents, audio recordings, videos, business files, and the like.
While the subject matter described herein is presented in the general context of program modules that execute in conjunction with the execution of an operating system and application programs on a computer system, those skilled in the art will recognize that other implementations may be performed in combination with other types of program modules. Generally, program modules include routines, programs, components, data structures, and other types of structures that perform particular tasks or implement particular abstract data types. Moreover, those skilled in the art will appreciate that the subject matter described herein may be practiced with other computer system configurations, including hand-held devices, multiprocessor systems, microprocessor-based or programmable consumer electronics, minicomputers, mainframe computers, and the like.
In the following detailed description, references are made to the accompanying drawings that form a part hereof, and in which are shown by way of illustration specific configurations or examples. Referring now to the drawings, in which like numerals represent like elements throughout the several figures, aspects of a computing system, computer-readable storage medium, and computer-implemented methodologies for analyzing data to determine an upload account. As will be described in more detail below with respect to FIGS. 4-6, there are a number of applications and services that can embody the functionality and techniques described herein.
FIG. 1 is a system diagram showing aspects of one illustrative mechanism disclosed herein for analyzing data to determine an upload account. As shown in FIG. 1, a system 100 may include a computing device 101, a server computer 110 and a network 120. The computing device 101 may operate as a stand-alone device, or the computing device 101 may operate in conjunction with the server computer 110 to perform the techniques disclosed herein. For illustrative purposes, one computing device 101 is shown in FIG. 1 and it can be appreciated that more computing devices 101 may be used in implementations of the described techniques.
It can also be appreciated that the server computer 110 may be a personal computing device, a server or a number of computing devices configured to perform the techniques described herein. It can be also appreciated that the server computer 110 and the computing device 101 are interconnected through one or more local and/or wide area networks, such as the network 120. It should be appreciated that many more network connections may be utilized than illustrated in FIG. 1.
The computing device 101 may include memory 180 storing data associated with a first data file 113, a second data file 114, and a program module 111. In addition, the server computer 110 may also include memory 181 storing data associated with a personal account 115 a business account 116 for a user, and a server program module 105. While one personal account 115 and one business account 116 is illustrated, more business accounts and personal accounts may be maintained and associated with a user. According to some techniques, the personal account 115 and the business account 116 are maintained and managed by the functionality of a storage service, such as the GOOGLE DRIVE storage service from GOOGLE, Inc., the DROPBOX storage service from DROPBOX or the ONEDRIVE storage service from MICROSOFT CORPORATION.
The computing device 101 may also store one or more program modules, such as program module 111, which may be in the form of a stand-alone application, a productivity application, an operating system component or any other application or software module having features that interact with a user via one or more input devices, such as the input devices 119. The input devices may be any device, such as a keyboard, and/or an interface (118), which may include a touch-enabled screen configured to receive gestures from one or more users. Each program module 111 may also be configured to manage and process the files and/or other data communicated with other computing devices. In the examples described in more detail below, each program module 111 may be configured to provide access to data, such as the data files (113 and 114) and note files data to upload to an account associated with a user of computing device 101.
In some configurations, techniques disclosed herein cause data to be analyzed to determine whether the data is to be uploaded to a first account associated with a user (e.g., a business account) or a second account associated with the user (e.g., a personal account). In some examples, more than two accounts may be associated with the user. In this case, the system 100 analyzes the data to upload when selecting the appropriate account.
Some illustrative configurations involve receiving a request to upload data from a computing device. For instance, the data to upload may be a first data file 113, such as a photograph taken by the computing device 101. Similarly, the data to upload may be a second data file 114, or some other data. In the current example, the first data file 113 (in this example, a photograph) has been requested to upload to one of the personal account 115 or the business account 116 of a remote storage service.
Instead of prompting the user to manually select whether to upload the photograph to the personal account 115 or the business account 116, the techniques described herein performed by the server program module 105, or some other component, determine whether the photograph is a photograph related to the business of the user, or whether the photograph is personal.
According to some configurations, the server program module 105 uses information identified from the data, as well as other information obtained from other data sources and/or other programs or applications associated with the user in determining what account to select. In the current example, the server program module 105 can identify from an analysis of the photograph, or metadata of the photograph, information such as but not limited to the time of day and the date the photograph was taken; the people (e.g., family, friends, co-workers) and/or objects recognized in the photograph (e.g., a cake, a car, . . . ); the location of where the photograph was taken; text extracted from the photograph; the type of business the user is involved in, and the like.
As discussed briefly above, the server program module 105 can also use information identified from other sources, such as one or more of external data sources (not shown) to determine whether the data is related to business or is personal. For instance, the system can access and analyze data from calendar programs (e.g., is the person in a business meeting or a personal meeting when the data was created?), organization charts (e.g., who are other people in the business, what level are they, how are they related?), contact lists (e.g., personal contact list and/or business contact list), social networks, document management platforms, and the like to provide more indications of whether the data is personal or business related. According to some techniques, the system can use on or more machine learning mechanisms (See FIG. 2) to assist in determining whether to upload the data to a business account or a personal account.
When the server program module 105 determines that the photograph is personal, the server program module 105 causes the data associated with the photograph to be uploaded to the personal account 115. When the photograph is determined by the server program module 105 to be business related, the photograph is uploaded to the business account. 116 Similar determinations can be made regarding other types of data, such as but not limited to word-processing documents, audio recordings, videos, and the like. More details are provided below with reference to FIGS. 2-6.
FIG. 2 is a system diagram showing aspects of one illustrative mechanism of a server program module 105 disclosed herein for analyzing data to determine an upload account. As shown in FIG. 2, system 200 illustrates server program module 105 in communication with external data sources 202A-202N. Server program module 105 includes machine learning mechanism 205, data extractor 210, and data receiver 204.
As described above, techniques disclosed herein cause an account to be selected based on the data being uploaded to a storage service, or some other computing device. As an example, when the data to upload is a photograph, the data extractor 210 of system 200 can extract features of the photo such as but not limited to the time of day and the date the photo was taken; the people (e.g., family, friends, co-workers) and/or objects recognized in the photo (e.g., a cake, a car, . . . ); the location of where the photograph was taken; text extracted from the photo; the type of business the user is involved in, and the like. In some configurations, the data extractor 210 can also access metadata associated with the uploaded data.
The metadata can provide more information about the photograph. In many cases, the metadata includes information such as but not limited to date information related to when the file was created, the author of the file, the type of file, user inputted keywords associated with the file, and the like. When the data is some other type of data (e.g., a word-processing document), the data extractor 210 may analyze the contents of the document as well as metadata associated with the document.
In some configurations, data obtained from other sources, such as one or more of external data sources 202A-202N, can be used to assist the system 200 in determining whether the data is personal or business related. For instance, the system 200 can access and analyze data from calendar programs (e.g., is the person in a business meeting or a personal meeting when the data was created?), organization charts (e.g., who are other people in the business, what level are they, how are they related?), contact lists (e.g., personal contact list and/or business contact list), social networks, document management platforms, and the like to provide more indications of whether the data is personal or business related.
As an example, if the first data file 113 is a photograph of a cake, and the user is employed by a bakery (e.g., as determined from user data, an organizational chart, or some other data source), the system 200 can determine that the photograph is business related. Similarly, if the first data file 113 is a word-processing document that includes references to baking terms or other individuals that are part of the bakery (e.g., as determined by the organizational chart), the system 200 can determine that the document is a business document that should be uploaded to the business account of the user. In another example, the system 200 can determine that a photograph to be uploaded may be taken during lunch hours for the user that includes people that are not employed by the bakery. In this case, the system 200 determines to upload the picture to the personal account for the user.
According to some techniques, the system 200 can use one or more machine learning mechanisms, such as machine learning mechanism 205, to assist in determining whether to upload the data to a business account or a personal account, or some other account. The machine learning mechanism 205 employed can be based on the type of data to upload and/or on other characteristics of the data. Generally, machine learning is a type of artificial intelligence that provides computers with the ability to recognize patterns and to use those patterns to perform actions on the data. The machine learning mechanisms utilized can be trained using supervised and/or unsupervised learning. Similarly, the machine learning technique can employ statistical analysis and or predictive analysis. Some types of machine learning mechanisms that can be utilized to select the account include but are not limited to decision tree learning; association rule learning; artificial neural networks; deep learning; inductive logic programming; support vector machines; clustering; Bayesian networks; reinforcement learning; representation learning; manifold learning algorithms; similarity learning; sparse dictionary learning; genetic algorithms; rule-based machine learning; learning classifier systems; and the like. The machine learning mechanism 205 provides an indication of an account selection 212 that indicates to the account to store the uploaded data.
In some configurations, the system 200 can determine and recommend users with whom to share the data (e.g., the first data file 113). For example, techniques disclosed herein can cause the contents of a file, such as the first file data 113 to be analyzed to determine the subject matter, keywords, individuals identified by the contents of the first data file 113, and the like.
Some illustrative configurations involve analyzing the content of the data file, such as the first data file 113, to determine keywords contained within the file and/or determined from an analysis of graphical content of the file. For instance, the system 200 can identify individuals within photographic data by performing a graphical analysis of the photographic data. The keywords can provide an indication of the subject matter of the document.
In addition to analyzing the content, the system 200 can also utilize other types of data in determining the users to recommend to share the document with. For example, the system 200 can utilize organizational charts, contact lists, calendar data, data identifying other users who have created documents with the same or similar content, data from social networks or other resources, and the like. Some of this data may be obtained from one or more of the external data sources 202A-202N.
After analyzing the contents of the document, the system 200 can identify and recommend other users to share the document with based on various criteria. For example, the system 200 can identify: users who have edited or worked on documents with similar subject matter; users with whom the user has previously shared similar documents with; users who are in the same work area (or area of interest as indicated by the contents of the document), and the like. The sharing recommendations can be for many different types of documents, such as but not limited to word-processing documents, email messages, other types of messages or electronic notes, music files, audio files, video files, and the like.
According to some examples, the system 200 displays the sharing recommendations (e.g., on a display) identifying the one or more recommended users. In some configurations, the sharing recommendations also provide a brief explanation of why the one or more users are recommended. For instance, the system 200 could indicate that the user is a co-worker of the user uploading the file or that the user is identified from an analysis of the contents of the file.
Some illustrative configurations involve receiving a request to share a document with a user, or users, from a computing device. For instance, a user may request to share a document with one or more other users. In other examples, the request can be generated by a program associated with the document in response to some other action or event (e.g., uploading a document to an account of a storage service, selecting a send option, a save option, and the like). In some cases, the system 200 can provide the sharing recommendations before, during, or after determining the account to select.
In some examples, the server program module 105 parses the contents of the first data file 113 to determine keywords and/or determine subjects included within the document. After determining the keywords, and/or the subjects of the document, the server program module 105 accesses data from one or more external data sources 202A-202N to determine other users that may be interested in the first data file 113. For instance, the data from the external data source(s) may indicate that a certain user performs work that is the same or similar subject matter of the file 113. In another example, a user creating a document may be a baker and the document being authored may relate to baking a cake.
As described above, techniques disclosed herein cause sharing recommendations to be provided to a user based on the content of a file. As an example, when the data to upload includes text, the data extractor 210 of system 200 can extract keywords of the first data file 113 that indicate the context of the document (e.g., provides the subject or subjects included within the document). In some configurations, the data extractor 210 can also access metadata associated with the first data file 113. When the data is some other type of data (e.g., a picture), the data extractor 210 may perform some object recognition techniques to identify the subject or to associate keywords with the photograph. The data extractor 210 may also perform some optical character recognition techniques to identify the subject or to associate keywords with the photograph. Although some of the examples disclosed herein involve the extraction of keywords, the techniques disclosed herein can involve the extraction of other features, such as, but not limited to, a file format, a file type, or a structure of the document (e.g. resume, receipt, recipe). Other metadata can be extracted from a document, such as, but not limited to, author data, an extension of the file, last modified date, creation date, last access date, etc. Such data and any other data that can be extracted from a document can be used in the techniques disclosed herein to generate sharing recommendations.
As discussed above, in some configurations, data obtained from other sources, such as one or more of external data sources 202A-202N, can be used to assist the system 200 in determining the users to recommend. For instance, the system 200 can access and analyze data from calendar programs (e.g., are other persons included in meetings relating to one or more of the keywords), organization charts (e.g., who are other people in the business that perform jobs related to the content of the document), contact lists (e.g., personal and/or business), social networks, document management platforms, and the like to provide more indications of users to recommend.
As an example, if the first data file 113 is a photograph showing a cake, and the user is employed by a bakery (e.g., as determined from user data, an organizational chart, or some other source), the system 200 can determine, via the external data sources, other employees of the bakery that are involved in baking a cake. Similarly, if the first data file 113 is a word-processing document that includes references to baking terms or other individuals that are part of the bakery (e.g., as determined by the organizational chart), the system 200 can generate keywords relating to the content of the first data file 113 and use those keywords to locate users that are associated with those keywords. In some examples, the contents of the first data file 113 and/or data from one or more external data sources 202A-202N indicate an association between a user and the file. For instance, the data from the first data file 113 and/or the data from the external data sources 202A-202N may provide information such as but not limited to job title, job responsibilities, interests, documents created, documents edited, users with whom the user has shared similar documents with, and the like. Based on the analysis of the first data file 113 and the data from the external data sources 202A-202N, the server program module 105, or some other component, provides the sharing recommendations that can be displayed within a user interface (not shown). According to some techniques, in addition to providing the name of the recommendations, the system 200 also provides a reason as to why the user was selected as a recommendation. For example, the user interface can show that a recommended user has created or edited similar content, another recommended user has previously shared a similar document with the user uploading the first data file 113, and yet another user is a co-worker that performs similar duties, or is somehow related to the work of baking a cake. Many other reasons can be provided. According to some techniques, the system 200 can use on or more machine learning mechanisms, such as machine learning mechanism 205, to assist in determining the sharing recommendations
Turning now to FIG. 3, aspects of a routine 300 for analyzing data to determine an upload account are shown and described below. It should be understood that the operations of the methods disclosed herein are not necessarily presented in any particular order and that performance of some or all of the operations in an alternative order(s) is possible and is contemplated. The operations have been presented in the demonstrated order for ease of description and illustration. Operations may be added, omitted, and/or performed simultaneously, without departing from the scope of the appended claims.
It also should be understood that the illustrated methods can be ended at any time and need not be performed in its entirety. Some or all operations of the methods, and/or substantially equivalent operations, can be performed by execution of computer-readable instructions included on a computer-storage media, as defined below. The term “computer-readable instructions,” and variants thereof, as used in the description and claims, is used expansively herein to include routines, applications, application modules, program modules, programs, components, data structures, algorithms, and the like. Computer-readable instructions can be implemented on various system configurations, including single-processor or multiprocessor systems, minicomputers, mainframe computers, personal computers, hand-held computing devices, microprocessor-based, programmable consumer electronics, combinations thereof, and the like.
Thus, it should be appreciated that the logical operations described herein are implemented (1) as a sequence of computer implemented acts or program modules running on a computing system and/or (2) as interconnected machine logic circuits or circuit modules within the computing system. The implementation is a matter of choice dependent on the performance and other requirements of the computing system. Accordingly, the logical operations described herein are referred to variously as states, operations, structural devices, acts, or modules. These operations, structural devices, acts, and modules may be implemented in software, in firmware, in special purpose digital logic, and any combination thereof.
As will be described in more detail below, in conjunction with FIGS. 4-6, the operations of the routine 300 are described herein as being implemented, at least in part, by an application, such as the server program module 105 and/or the program module 111, or some other program module. Although the following illustration refers to the server program module 105, it can be appreciated that the operations of the routine 300 may be also implemented in many other ways. For example, the routine 300 may be implemented by the use of an application such as a content creation application or data processing application, e.g., a photograph application, a word processing application, a spreadsheet application, etc. In addition, one or more of the operations of the routine 300 may alternatively or additionally be implemented, at least in part, by a web browser application 410 of FIG. 4 or another application working in conjunction with one or more application servers 508 of FIG. 5.
With reference to FIG. 3, the routine 300 begins at operation 302, where the server program module 105 receives a request to store data. As summarized above, a user may request to store data in an account of a user maintained by a remote storage service. The data may include any type of data stored in any format. For instance, the stored data may include a photo, an audio file, a video file, a word-processing document, or some other type of file or data. Other metadata can be generated when the data is created or stored. For instance, a device having a location component, such as a GPS component, can be used for generating location data. Time stamps, modified dates, author information and other metadata can be stored in association with the stored data. In one illustrative example, operation 302 may involve a process of receiving an instruction or command from a computing device 101 associated with a user to store data. The scope of the present disclosure includes any instruction, command or data that may be received that requests the data to be uploaded.
At operation 304, the server program module 105 analyzes the data. As described above, the server program module 105 may identify the subject(s) of the data as well as determine other keywords or information about the data. In some examples, the server program module 105 performs one or more facial recognition and/or object recognition techniques to identify people and objects depicted within a photo. The server program module 105 may analyze the received data and generate contextual information that may be used to make an association between the data and an account. In addition, the contextual information may be generated or modified depending on the functions that are performed on the data. For instance, if a user is creating a document at work, and the document has one or more work-related keywords, the document or portions of the document may be associated with a business account. In another example, if a document is created at an office location but the list includes a shopping list, the document containing the shopping list may be associated with a different account, such as a user's personal account.
At 306, one or more other data sources may be accessed by the system 100 to assist in selecting the upload account. As discussed above, the server program module 105, can access other data such as, but not limited to, calendar programs, organization charts, contact lists, social networks, document management platforms, and the like. The other data is referred to herein as contextual data. The contextual data can include, but is not limited to calendar data, organizational data, contact data, social network data, and document management data. The server program module 105 can analyze this data to determine whether the data to upload is associated with personal or business contact, created during a business meeting or during personal time (e.g., a vacation), and the like. For example, the server program module 105 can determine that an image to upload is business related when the image includes faces of business contacts as compared to personal contacts. In some configurations, the server program module 105 can utilize multiple data sources when determining the upload account. For example, the server program module 105 can utilize a calendar program to determine when and where the meeting is occurring when the data was created as well who is attending the meeting. The server program module 105 can then reference the contact lists of the user, along with the organization charts, to determine whether the attendees at the meeting where personal contacts or business contacts. The server program module 105 can be programmed many different ways to make this analysis.
Next, the routine 308, the system 100 selects the account to store the received data. As discussed above, the system can utilize a machine learning mechanism 205 to select the account. As can be appreciated, operation 308 may involve a number of different factors or conditions for selecting an upload account. In some configurations, contextual information generated from one or more actions or conditions, such as an action of the user or an action of the requesting computing device, may be used in conjunction with the location information to select the account. In addition, a user setting or default setting may have one or more conditions or instructions that cause the selection of one or more subsets of data based on location information and/or other contextual information. In some configurations, the factors for selecting an upload account can be weighted.
At 310, the data requested to be uploaded is associated with the selected account. As discussed above, the server program module 105 can store the data into an account associated with the user without explicitly requesting the user to select the account. In some examples, the server program module 105 stores the data into a personal account of a storage service or a business account of the storage service. Once the data is stored within the selected account, the routine 300 terminates.
FIG. 4 shows additional details of an example computer architecture 400 for a computer, such as the computing device 101 (FIG. 1), capable of executing the program components described above analyzing data to determine an upload account. Thus, the computer architecture 400 illustrated in FIG. 4 illustrates an architecture for a server computer, mobile phone, a PDA, a smart phone, a desktop computer, a netbook computer, a tablet computer, and/or a laptop computer. The computer architecture 400 may be utilized to execute any aspects of the software components presented herein.
The computer architecture 400 illustrated in FIG. 4 includes a central processing unit 402 (“CPU”), a system memory 404, including a random access memory 406 (“RAM”) and a read-only memory (“ROM”) 408, and a system bus 410 that couples the memory 404 to the CPU 402. A basic input/output system containing the basic routines that help to transfer information between elements within the computer architecture 400, such as during startup, is stored in the ROM 408. The computer architecture 400 further includes a mass storage device 412 for storing an operating system 407, and one or more application programs including, but not limited to, the application 413, program module 111, and a web browser application 410. The illustrated mass storage device 412 may also store a file 411, which may in any format containing any type of information, note data, word document data, spreadsheet data, etc.
The mass storage device 412 is connected to the CPU 402 through a mass storage controller (not shown) connected to the bus 410. The mass storage device 412 and its associated computer-readable media provide non-volatile storage for the computer architecture 400. 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 architecture 400.
Communication media includes 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 includes any delivery media. The term “modulated data signal” means a signal that has one or more of its characteristics changed or set in a manner as to encode information in the signal. By way of example, and not limitation, communication media includes wired media such as a wired network or direct-wired connection, and wireless media such as acoustic, RF, infrared and other wireless media. Combinations of the any of the above should also be included within the scope of computer-readable media.
By way of example, and not limitation, computer storage media may include volatile and non-volatile, removable and non-removable media implemented in any method or technology for storage of information such as computer-readable instructions, data structures, program modules or other data. For example, computer media includes, but is not limited to, RAM, ROM, EPROM, EEPROM, flash memory or other solid state memory technology, CD-ROM, digital versatile disks (“DVD”), HD-DVD, BLU-RAY, or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and which can be accessed by the computer architecture 400. For purposes the claims, the phrase “computer storage medium,” “computer-readable storage medium” and variations thereof, does not include waves, signals, and/or other transitory and/or intangible communication media, per se.
According to various configurations, the computer architecture 400 may operate in a networked environment using logical connections to remote computers through the network 456 and/or another network (not shown). The computer architecture 400 may connect to the network 456 through a network interface unit 414 connected to the bus 410. It should be appreciated that the network interface unit 414 also may be utilized to connect to other types of networks and remote computer systems. The computer architecture 400 also may include an input/output controller 416 for receiving and processing input from a number of other devices, including a keyboard, mouse, or electronic stylus (not shown in FIG. 4). Similarly, the input/output controller 416 may provide output to a display screen, a printer, or other type of output device (also not shown in FIG. 4).
It should be appreciated that the software components described herein may, when loaded into the CPU 402 and executed, transform the CPU 402 and the overall computer architecture 400 from a general-purpose computing system into a special-purpose computing system customized to facilitate the functionality presented herein. The CPU 402 may 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 CPU 402 may 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 CPU 402 by specifying how the CPU 402 transitions between states, thereby transforming the transistors or other discrete hardware elements constituting the CPU 402.
Encoding the software modules presented herein also may transform the physical structure of the computer-readable media presented herein. The specific transformation of physical structure may depend on various factors, in different implementations of this description. Examples of such factors may include, but are not limited to, the technology used to implement the computer-readable media, whether the computer-readable media is characterized as primary or secondary storage, and the like. For example, if the computer-readable media is implemented as semiconductor-based memory, the software disclosed herein may be encoded on the computer-readable media by transforming the physical state of the semiconductor memory. For example, the software may transform the state of transistors, capacitors, or other discrete circuit elements constituting the semiconductor memory. The software also may transform the physical state of such components in order to store data thereupon.
As another example, the computer-readable media disclosed herein may be implemented using magnetic or optical technology. In such implementations, the software presented herein may transform the physical state of magnetic or optical media, when the software is encoded therein. These transformations may include altering the magnetic characteristics of particular locations within given magnetic media. These transformations also may include altering the physical features or characteristics of particular locations within given optical media, to change the optical characteristics of those locations. Other transformations of physical media are possible without departing from the scope and spirit of the present description, with the foregoing examples provided only to facilitate this discussion.
In light of the above, it should be appreciated that many types of physical transformations take place in the computer architecture 400 in order to store and execute the software components presented herein. It also should be appreciated that the computer architecture 400 may 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 architecture 400 may not include all of the components shown in FIG. 4, may include other components that are not explicitly shown in FIG. 4, or may utilize an architecture completely different than that shown in FIG. 4.
FIG. 5 depicts an illustrative distributed computing environment 500 capable of executing the software components described herein for analyzing data to determine an upload account, among other aspects. Thus, the distributed computing environment 500 illustrated in FIG. 5 can be utilized to execute any aspects of the software components presented herein. For example, the distributed computing environment 500 can be utilized to execute aspects of the program module 111 and/or other software components described herein.
According to various implementations, the distributed computing environment 500 includes a computing environment 502 operating on, in communication with, or as part of the network 456. The network 504 may be or may include the network 456, described above with reference to FIG. 4. The network 504 also can include various access networks. One or more client devices 506A-506N (hereinafter referred to collectively and/or generically as “clients 506”) can communicate with the computing environment 502 via the network 504 and/or other connections (not illustrated in FIG. 5). In one illustrated configuration, the clients 506 include a computing device 506A such as a laptop computer, a desktop computer, or other computing device; a slate or tablet computing device (“tablet computing device”) 506B; a mobile computing device 506C such as a mobile telephone, a smart phone, or other mobile computing device; a server computer 506D; and/or other devices 506N. It should be understood that any number of clients 506 can communicate with the computing environment 502. Two example computing architectures for the clients 506 are illustrated and described herein with reference to FIGS. 4 and 6. It should be understood that the illustrated clients 506 and computing architectures illustrated and described herein are illustrative, and should not be construed as being limited in any way.
In the illustrated configuration, the computing environment 502 includes application servers 508, data storage 510, and one or more network interfaces 512. According to various implementations, the functionality of the application servers 508 can be provided by one or more server computers that are executing as part of, or in communication with, the network 504. The application servers 508 can host various services, virtual machines, portals, and/or other resources. In the illustrated configuration, the application servers 508 host one or more virtual machines 514 for hosting applications or other functionality. According to various implementations, the virtual machines 514 host one or more applications and/or software modules for analyzing data to determine an upload account. It should be understood that this configuration is illustrative, and should not be construed as being limiting in any way. The application servers 508 also host or provide access to one or more portals, link pages, Web sites, and/or other information (“Web portals”) 516.
According to various implementations, the application servers 508 also include one or more mailbox services 518 and one or more messaging services 520. The mailbox services 518 can include electronic mail (“email”) services. The mailbox services 518 also can include various personal information management (“PIM”) services including, but not limited to, calendar services, contact management services, collaboration services, and/or other services. The messaging services 520 can include, but are not limited to, instant messaging services, chat services, forum services, and/or other communication services.
The application servers 508 also may include one or more social networking services 522. The social networking services 522 can 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 services 522 are 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 services 522 are 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 services 522 also 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 services 522 are 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 services 522 may host one or more applications and/or software modules for providing the functionality described herein. For instance, any one of the application servers 508 may 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 client 506 may communicate with a networking service 522 and facilitate the functionality, even in part, described above with respect to FIG. 3.
As shown in FIG. 5, the application servers 508 also can host other services, applications, portals, and/or other resources (“other resources”) 524. The other resources 524 can include, but are not limited to, OCR or spreadsheet display functionality. It thus can be appreciated that the computing environment 502 can provide integration of the concepts and technologies disclosed herein provided herein with various mailbox, messaging, social networking, and/or other services or resources.
As mentioned above, the computing environment 502 can include the data storage 510. According to various implementations, the functionality of the data storage 510 is provided by one or more databases operating on, or in communication with, the network 504. The functionality of the data storage 510 also can be provided by one or more server computers configured to host data for the computing environment 502. The data storage 510 can include, host, or provide one or more real or virtual datastores 526A-526N (hereinafter referred to collectively and/or generically as “datastores 526”). The datastores 526 are configured to host data used or created by the application servers 508 and/or other data. Although not illustrated in FIG. 5, the datastores 526 also can host or store note files, word files, spreadsheet files, data structures, algorithms for execution by a recommendation engine, and/or other data utilized by any application program or another module, such as the program module 111. Aspects of the datastores 526 and/or data within the datastores 526 may be associated with data defining one or more geographic locations and/or a geographic area.
The computing environment 502 can communicate with, or be accessed by, the network interfaces 512. The network interfaces 512 can include various types of network hardware and software for supporting communications between two or more computing devices including, but not limited to, the clients 506 and the application servers 508. It should be appreciated that the network interfaces 512 also may be utilized to connect to other types of networks and/or computer systems.
It should be understood that the distributed computing environment 500 described 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 environment 500 provides the software functionality described herein as a service to the clients 506. It should be understood that the clients 506 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 environment 500 to utilize the functionality described herein, among other aspects. In one specific example, as summarized above, techniques described herein may be implemented, at least in part, by the web browser application 410 of FIG. 4, which works in conjunction with the application servers 508 of FIG. 5.
Turning now to FIG. 6, an illustrative computing device architecture 600 for a computing device that is capable of executing various software components described herein for analyzing data to determine an upload account. The computing device architecture 600 is 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 architecture 600 is applicable to any of the clients 506 shown in FIG. 5. Moreover, aspects of the computing device architecture 600 may be applicable to traditional desktop computers, portable computers (e.g., laptops, notebooks, ultra-portables, and netbooks), server computers, and other computer systems, such as described herein with reference to FIG. 4. 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 architecture 600 illustrated in FIG. 6 includes a processor 602, memory components 604, network connectivity components 606, sensor components 608, input/output components 610, and power components 612. In the illustrated configuration, the processor 602 is in communication with the memory components 604, the network connectivity components 606, the sensor components 608, the input/output (“I/O”) components 610, and the power components 612. Although no connections are shown between the individuals components illustrated in FIG. 6, 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 processor 602 includes 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 architecture 600 in order to perform various functionality described herein. The processor 602 may 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 processor 602 includes 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 processor 602 is configured to communicate with a discrete GPU (not shown). 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 processor 602 is, 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 processor 602, a GPU, one or more of the network connectivity components 606, and one or more of the sensor components 608. In some configurations, the processor 602 is fabricated, in part, utilizing a package-on-package (“PoP”) integrated circuit packaging technique. The processor 602 may be a single core or multi-core processor.
The processor 602 may be created in accordance with an ARM architecture, available for license from ARM HOLDINGS of Cambridge, United Kingdom. Alternatively, the processor 602 may be created in accordance with an ×86 architecture, such as is available from INTEL CORPORATION of Mountain View, Calif. and others. In some configurations, the processor 602 is 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 components 604 include a random access memory (“RAM”) 614, a read-only memory (“ROM”) 616, an integrated storage memory (“integrated storage”) 618, and a removable storage memory (“removable storage”) 620. In some configurations, the RAM 614 or a portion thereof, the ROM 616 or a portion thereof, and/or some combination the RAM 614 and the ROM 616 is integrated in the processor 602. In some configurations, the ROM 616 is 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 storage 618 and/or the removable storage 620.
The integrated storage 618 can include a solid-state memory, a hard disk, or a combination of solid-state memory and a hard disk. The integrated storage 618 may be soldered or otherwise connected to a logic board upon which the processor 602 and other components described herein also may be connected. As such, the integrated storage 618 is integrated in the computing device. The integrated storage 618 is configured to store an operating system or portions thereof, application programs, data, and other software components described herein.
The removable storage 620 can include a solid-state memory, a hard disk, or a combination of solid-state memory and a hard disk. In some configurations, the removable storage 620 is provided in lieu of the integrated storage 618. In other configurations, the removable storage 620 is provided as additional optional storage. In some configurations, the removable storage 620 is logically combined with the integrated storage 618 such that the total available storage is made available as a total combined storage capacity. In some configurations, the total combined capacity of the integrated storage 618 and the removable storage 620 is shown to a user instead of separate storage capacities for the integrated storage 618 and the removable storage 620.
The removable storage 620 is configured to be inserted into a removable storage memory slot (not shown) or other mechanism by which the removable storage 620 is inserted and secured to facilitate a connection over which the removable storage 620 can communicate with other components of the computing device, such as the processor 602. The removable storage 620 may 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 components 604 can store an operating system. According to various configurations, the operating system includes, but is not limited to, SYMBIAN OS from SYMBIAN LIMITED, WINDOWS MOBILE OS from Microsoft Corporation of Redmond, Wash., WINDOWS PHONE OS from Microsoft Corporation, WINDOWS from Microsoft Corporation, PALM WEBOS 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 components 606 include a wireless wide area network component (“WWAN component”) 622, a wireless local area network component (“WLAN component”) 624, and a wireless personal area network component (“WPAN component”) 626. The network connectivity components 606 facilitate communications to and from the network 656 or another network, which may be a WWAN, a WLAN, or a WPAN. Although only the network 656 is illustrated, the network connectivity components 606 may facilitate simultaneous communication with multiple networks, including the network 504 of FIG. 5. For example, the network connectivity components 606 may facilitate simultaneous communications with multiple networks via one or more of a WWAN, a WLAN, or a WPAN.
The network 656 may 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 architecture 600 via the WWAN component 622. The mobile telecommunications technologies can include, but are not limited to, Global System for Mobile communications (“GSM”), Code Division Multiple Access (“CDMA”) ONE, CDMA2000, Universal Mobile Telecommunications System (“UMTS”), Long Term Evolution (“LTE”), and Worldwide Interoperability for Microwave Access (“WiMAX”). Moreover, the network 656 may 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 network 64 may be configured to provide voice and/or data communications with any combination of the above technologies. The network 656 may be configured to or adapted to provide voice and/or data communications in accordance with future generation technologies.
In some configurations, the WWAN component 622 is configured to provide dual-multi-mode connectivity to the network 656. For example, the WWAN component 622 may be configured to provide connectivity to the network 656, wherein the network 656 provides service via GSM and UMTS technologies, or via some other combination of technologies. Alternatively, multiple WWAN components 622 may 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 component 622 may facilitate similar connectivity to multiple networks (e.g., a UMTS network and an LTE network).
The network 656 may 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 standard (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 component 624 is configured to connect to the network 656 via 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 network 656 may 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 component 626 is configured to facilitate communications with other devices, such as peripherals, computers, or other computing devices via the WPAN.
The sensor components 608 include a magnetometer 628, an ambient light sensor 630, a proximity sensor 632, an accelerometer 634, a gyroscope 636, and a Global Positioning System sensor (“GPS sensor”) 638. 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 architecture 600.
The magnetometer 628 is configured to measure the strength and direction of a magnetic field. In some configurations the magnetometer 628 provides measurements to a compass application program stored within one of the memory components 604 in 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 magnetometer 628 are contemplated.
The ambient light sensor 630 is configured to measure ambient light. In some configurations, the ambient light sensor 630 provides measurements to an application program stored within one the memory components 604 in 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 sensor 630 are contemplated.
The proximity sensor 632 is configured to detect the presence of an object or thing in proximity to the computing device without direct contact. In some configurations, the proximity sensor 632 detects 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 components 604 that 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 sensor 632 are contemplated.
The accelerometer 634 is configured to measure proper acceleration. In some configurations, output from the accelerometer 634 is 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 accelerometer 634. In some configurations, output from the accelerometer 634 is 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 accelerometer 634 are contemplated.
The gyroscope 636 is configured to measure and maintain orientation. In some configurations, output from the gyroscope 636 is used by an application program as an input mechanism to control some functionality of the application program. For example, the gyroscope 636 can 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 gyroscope 636 and the accelerometer 634 to enhance control of some functionality of the application program. Other uses of the gyroscope 636 are contemplated.
The GPS sensor 638 is configured to receive signals from GPS satellites for use in calculating a location. The location calculated by the GPS sensor 638 may be used by any application program that requires or benefits from location information. For example, the location calculated by the GPS sensor 638 may 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 sensor 638 may be used to provide location information to an external location-based service, such as E911 service. The GPS sensor 638 may obtain location information generated via WI-FI, WIMAX, and/or cellular triangulation techniques utilizing one or more of the network connectivity components 606 to aid the GPS sensor 638 in obtaining a location fix. The GPS sensor 638 may also be used in Assisted GPS (“A-GPS”) systems.
The I/O components 610 include a display 640, a touchscreen 642, a data I/O interface component (“data I/O”) 644, an audio I/O interface component (“audio I/O”) 646, a video I/O interface component (“video I/O”) 648, and a camera 650. In some configurations, the display 640 and the touchscreen 642 are combined. In some configurations two or more of the data I/O component 644, the audio I/O component 646, and the video I/O component 648 are combined. The I/O components 610 may include discrete processors configured to support the various interface described below, or may include processing functionality built-in to the processor 602.
The display 640 is an output device configured to present information in a visual form. In particular, the display 640 may 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 display 640 is a liquid crystal display (“LCD”) utilizing any active or passive matrix technology and any backlighting technology (if used). In some configurations, the display 640 is an organic light emitting diode (“OLED”) display. Other display types are contemplated.
The touchscreen 642, also referred to herein as a “touch-enabled screen,” is an input device configured to detect the presence and location of a touch. The touchscreen 642 may 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 touchscreen 642 is incorporated on top of the display 640 as a transparent layer to enable a user to use one or more touches to interact with objects or other information presented on the display 640. In other configurations, the touchscreen 642 is a touch pad incorporated on a surface of the computing device that does not include the display 640. For example, the computing device may have a touchscreen incorporated on top of the display 640 and a touch pad on a surface opposite the display 640.
In some configurations, the touchscreen 642 is a single-touch touchscreen. In other configurations, the touchscreen 642 is a multi-touch touchscreen. In some configurations, the touchscreen 642 is 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 touchscreen 642. As such, a developer may create gestures that are specific to a particular application program.
In some configurations, the touchscreen 642 supports a tap gesture in which a user taps the touchscreen 642 once on an item presented on the display 640. 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 touchscreen 642 supports a double tap gesture in which a user taps the touchscreen 642 twice on an item presented on the display 640. 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 touchscreen 642 supports a tap and hold gesture in which a user taps the touchscreen 642 and 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 touchscreen 642 supports a pan gesture in which a user places a finger on the touchscreen 642 and maintains contact with the touchscreen 642 while moving the finger on the touchscreen 642. 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 touchscreen 642 supports 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 touchscreen 642 supports a pinch and stretch gesture in which a user makes a pinching motion with two fingers (e.g., thumb and forefinger) on the touchscreen 642 or 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 website, map, or picture.
Although the above gestures have been described with reference to the use 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 touchscreen 642. 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 component 644 is 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 component 644 includes 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 component 646 is configured to provide audio input and/or output capabilities to the computing device. In some configurations, the audio I/O interface component 646 includes a microphone configured to collect audio signals. In some configurations, the audio I/O interface component 646 includes a headphone jack configured to provide connectivity for headphones or other external speakers. In some configurations, the audio I/O interface component 646 includes a speaker for the output of audio signals. In some configurations, the audio I/O interface component 646 includes an optical audio cable out.
The video I/O interface component 648 is configured to provide video input and/or output capabilities to the computing device. In some configurations, the video I/O interface component 648 includes 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 component 648 includes 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 component 648 or portions thereof is combined with the audio I/O interface component 646 or portions thereof.
The camera 650 can be configured to capture still images and/or video. The camera 650 may utilize a charge coupled device (“CCD”) or a complementary metal oxide semiconductor (“CMOS”) image sensor to capture images. In some configurations, the camera 650 includes a flash to aid in taking pictures in low-light environments. Settings for the camera 650 may be implemented as hardware or software buttons.
Although not illustrated, one or more hardware buttons may also be included in the computing device architecture 600. 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 components 612 include one or more batteries 652, which can be connected to a battery gauge 654. The batteries 652 may 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 batteries 652 may be made of one or more cells.
The battery gauge 654 can be configured to measure battery parameters such as current, voltage, and temperature. In some configurations, the battery gauge 654 is 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 gauge 654 provides 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 components 612 may also include a power connector, which may be combined with one or more of the aforementioned I/O components 610. The power components 612 may interface with an external power system or charging equipment via an I/O component.
Based on the foregoing, it should be appreciated that concepts and technologies have been disclosed herein that analyze data to determine an upload account. Although the subject matter presented herein has been described in language specific to computer structural features, methodological and transformative acts, specific computing machinery, and computer readable media, it is to be understood that the invention defined in the appended claims is not necessarily limited to the specific features, acts, or media described herein. Rather, the specific features, acts and mediums are disclosed as example forms of implementing the claims.
The subject matter described above is provided by way of illustration only and should not be construed as limiting. Various modifications and changes may be made to the subject matter described herein without following the example configurations and applications illustrated and described, and without departing from the true spirit and scope of the present invention, which is set forth in the following claims.

Claims

What is claimed is:

1. A computer-implemented method comprising:

obtaining, from a computing device associated with a user, a request to store a file;

obtaining contextual data from the one or more data sources that includes data associated with the user, wherein the contextual data includes one or more of a calendar data, organizational data, contact data, social network data, and document management data;

analyzing at least a portion of the file to and the contextual data obtained from the one or more data sources to determine whether the file is a personal category or a business category;

selecting a first account associated with the user and causing the file to be stored in the first account when it is determined that the file is the personal category; and

selecting a second account associated with the user and causing the file to be stored in the second account when it is determined that the file is the business category.

2. The computer-implemented method of claim 1, wherein analyzing the at least the portion of the file and the contextual data comprises identifying from the contextual data one or more of a business contact of the user or a business activity of the user that is identified from an analysis of the file.

3. The computer-implemented method of claim 1, wherein analyzing the at least the portion of the file and the second data comprises identifying keywords from the file and determining whether the keywords are business related, and wherein selecting the second account is based at least in part on determining that the keywords are business related.

4. The computer-implemented method of claim 1, wherein the file is photographic data, and wherein analyzing the file and the contextual data includes identifying an individual depicted within photographic data and determining from the contextual data that the individual is a business contact of the user or a personal contact of the user.

5. The computer-implemented method of claim 1, wherein the first account is a personal account and the second account is a business account.

6. The computer-implemented method of claim 1, wherein selecting the account comprises utilizing a machine learning mechanism.

7. The computer-implemented method of claim 1, further comprising determining one or more individuals to recommend to share the file with based, at least in part, on contents of the file and the analysis of the contextual data.

8. A computer, comprising:

a processor; and

a computer-readable storage medium in communication with the processor, the computer-readable storage medium having computer-executable instructions stored thereupon which, when executed by the processor, cause the computer to

obtain, from a remote computer, a request to store first data within an account of a storage service, wherein the account is associated with a user;

access second data associated with the user, wherein the second data includes one or more of a calendar data, organizational data, contact data, social network data, and document management data;

analyze the first data and the second data to determine that the first data is personal or business related;

select the account from at least a personal account and a business account; and

cause the first data to be stored in the account.

9. The computer of claim 8, wherein analyzing the first data and the second data comprises identifying from the contact data that the first data is associated with a business contact or a personal contact of the user.

10. The computer of claim 8, wherein analyzing the first data and the second data comprises identifying one or more individuals or keywords from the first data and determining that the one or more individuals or keywords are business related, and wherein selecting the account is based at least in part on determining that the one or more individuals or keywords are business related.

11. The computer of claim 8, wherein the first data is photographic data, and wherein analyzing the first data and the second data includes identifying one or more of an individual depicted within photographic data or a location associated with the photographic data.

12. The computer of claim 8, wherein selecting the account comprises utilizing a machine learning mechanism.

13. The computer of claim 8, wherein the computer-executable instructions further cause the computer to identify one or more individuals to share the first data with based, at least in part, on contents of the first data and the second data.

14. The computer of claim 13, wherein the computer-executable instructions further cause the computer to provide for display the identification of the one or more individuals.

15. A computer-readable storage medium having computer-executable instructions stored thereupon which, when executed by a computer, cause the computer to:

obtain, from a remote computer, a request to store a file in an account of a storage service;

obtain, from one or more data sources, second data associated with the user, wherein the second data includes one or more of a calendar data, organizational data, contact data, social network data, and document management data;

cause a machine learning mechanism to determine that the first data is personal or business related based, at least in part, on the first data and the second data;

select the account of the storage service from at least a personal account of the user and a business account of the user based at least in part on the determination that the first data is personal or business related; and

cause the first data to be stored in the account.

16. The computer-readable storage medium of claim 15, wherein the computer-executable instructions further cause the computer to identify keywords from the first data.

17. The computer-readable storage medium of claim 16, wherein the keywords are provided to the machine learning mechanism.

18. The computer-readable storage medium of claim 15, wherein the computer-executable instructions further cause the computer to identify from the first data an individual and determine that the individual is a business contact or a personal contact.

19. The computer-readable storage medium of claim 15, wherein the first data is photographic data and wherein the computer-executable instructions further cause the computer to identify an individual depicted by the photographic data and determine that the individual is a contact of the user.

20. The computer-readable storage medium of claim 1, wherein the computer-executable instructions further cause the computer to identify one or more individuals to share the first data with based, at least in part, on contents of the first data and the second data.