U.S. patent number 8,428,908 [Application Number 13/455,899] was granted by the patent office on 2013-04-23 for cognitive agent.
This patent grant is currently assigned to Microsoft Corporation. The grantee listed for this patent is Avi Bar-Zeev, Gur Kimchi, Stephen L. Lawler, Eyal Ofek, Leonard Smith. Invention is credited to Avi Bar-Zeev, Gur Kimchi, Stephen L. Lawler, Eyal Ofek, Leonard Smith.
United States Patent |
8,428,908 |
Lawler , et al. |
April 23, 2013 |
Cognitive agent
Abstract
Aspects relate to a cognitive agent that performs functions
associated with a desired result. The functions performed by
cognitive agent supplement other activities performed at a same
time. In such a manner, the cognitive agent can function as a
surrogate for a user. A performed activity can trigger
implementation of another activity that is an extension of the
performed activity. Cognitive agent can perform functions that can
be represented as an avatar. Further, cognitive agent can be
associated with a diagnostics component that evaluates an operating
condition. Based on the operating condition cognitive agent can
implement automatic actions associated with mitigating failures
and/or prolonging the life of machinery.
Inventors: |
Lawler; Stephen L. (Redmond,
WA), Ofek; Eyal (Redmond, WA), Kimchi; Gur (Bellevue,
WA), Smith; Leonard (Seattle, WA), Bar-Zeev; Avi
(Redmond, WA) |
Applicant: |
Name |
City |
State |
Country |
Type |
Lawler; Stephen L.
Ofek; Eyal
Kimchi; Gur
Smith; Leonard
Bar-Zeev; Avi |
Redmond
Redmond
Bellevue
Seattle
Redmond |
WA
WA
WA
WA
WA |
US
US
US
US
US |
|
|
Assignee: |
Microsoft Corporation (Redmond,
WA)
|
Family
ID: |
42785319 |
Appl.
No.: |
13/455,899 |
Filed: |
April 25, 2012 |
Prior Publication Data
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Document
Identifier |
Publication Date |
|
US 20120210171 A1 |
Aug 16, 2012 |
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Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
Issue Date |
|
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12415860 |
Mar 31, 2009 |
8195430 |
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Current U.S.
Class: |
702/184;
705/7.13; 706/62; 714/37; 704/270 |
Current CPC
Class: |
G06Q
10/0637 (20130101); G06Q 30/0273 (20130101); H04L
67/10 (20130101); G06N 3/006 (20130101) |
Current International
Class: |
G06F
11/30 (20060101); G21C 17/00 (20060101) |
Field of
Search: |
;702/182,183,184,186,188
;704/270 ;705/7.13,7.15,7.36,14.69 ;706/12,46,62 ;707/10 ;714/37
;715/706,762 |
References Cited
[Referenced By]
U.S. Patent Documents
Other References
Di Stefano, Antonella, et al., "A Multi-Agent Reflective
Architecture for Web Search Assistance" published online at
http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.19.2298&rep=repl-
&type=pdf; retrieved Jan. 24, 2009; 8 pages. cited by applicant
.
Akoulchina, Irina and Jean-Gabriel Ganascia, "SAGE Agent for the
SATELIT Web-based System" published online at
http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.38.7641&rep=rep1-
&type=pdf; retrieved Jan. 24, 2009; 6 pages. cited by applicant
.
Baylor, A.L., "Running Head: Intelligent Agents as Cognitive
Tools", Educational Technology, 1999, vol. 39, Issue 2, pp. 36-40,
published online at
[http://mailer.fsu.edu/.about.abaylor/PDF/cogtool.pdf], retrieved
Jan. 24, 2009. cited by applicant .
Rhodes, B.J. and P. Maes, "Just-in-Time Information Retrieval
Agents", IBM Systems Journal, 2000, vol. 39, Nos. 3 & 4,
published online at
[http://www.research.ibm.com/journal/sj/393/part2/rhodes.html],
retrieved Jan. 24, 2009; 16 pages. cited by applicant.
|
Primary Examiner: Le; John H
Attorney, Agent or Firm: Shook Hardy & Bacon LLP
Parent Case Text
CROSS-REFERENCE TO RELATED APPLICATIONS
This application is a continuation of prior application Ser. No.
12/415,860, filed on Mar. 31, 2009 and entitled "COGNITIVE AGENT."
Claims
What is claimed is:
1. A method performed by a computing device for automatically
providing requested information to supplement other activities
performed at a same time, comprising: receiving a request for one
or more actions to be performed without user intervention; the
computing device performing at least one action and evaluating
results of the action; determining a user environment before the
results are presented; and displaying the results of the action to
a user, wherein the at least one action is performed while the user
is performing another action, and wherein a presentation of the
results is modified based on the user environment.
2. The method of claim 1, wherein the results are displayed using a
virtual avatar.
3. The method of claim 1, wherein the request is inferred from
observance of a user-initiated activity being performed.
4. The method of claim 1, further comprising operating a cognitive
agent to function as a surrogate for the user.
5. The method of claim 1, further comprising analyzing user actions
and inferring the request based on a user action.
6. The method of claim 1, further comprising analyzing user actions
and inferring the request based on a user inaction.
7. The method of claim 1, further comprising modifying a displayed
user interface based the results.
8. The method of claim 1, further comprising altering the display
of the results as a function of device parameters.
9. A system that facilitates automatic implementation of actions,
comprising: a receiver component that accepts a request for one or
more actions to be performed without user intervention; a cognitive
agent component that automatically performs at least one action and
evaluates the results of the action; a context component that
evaluates a user environment before the results are presented; and
an output component that presents the results of the action to a
user, wherein the at least one action is performed while the user
is performing another action and wherein a presentation of the
results is modified based on the user environment.
10. The system of claim 9, the cognitive agent operates as a
surrogate for the user.
11. The system of claim 9, further comprising an observation
component that analyzes user actions and infers the request based
on a user action or a user inaction.
12. The system of claim 9, the cognitive agent accesses at least
one of preferences, historical data, parameters, or combinations
thereof to perform the at least one action.
13. The system of claim 9, further comprising a context component
that ascertains a device type and alters the presented results as a
function of the device type.
14. The system of claim 9, the results are presented though
implementation of an avatar.
15. The system of claim 9, the request for one or more actions is
implementation of diagnostics on at least one piece of
machinery.
16. A computer-readable memory device storing computer-executable
instructions that, when executed, perform a method for
automatically providing requested information to supplement other
activities performed at a same time, the method comprising:
inferring a request to perform an action without user intervention,
the request being inferred from a user-initiated activity;
generating results of the action while the user-initiated action is
performed; determining a user environment before the results are
presented; and displaying the results of the action, wherein a
presentation of the results is modified based on the user
environment.
17. The device of claim 16, wherein the results are presented
though implementation of an avatar and wherein the presentation of
the results by the avatar is modified based on the user
environment.
Description
BACKGROUND
With the advanced computing technologies available today, more and
more people demand instant access to data and other information.
Further, since such a vast amount of data is readily available,
people tend to become more inquisitive and search for information
on a large variety of topics and/or locations. At times, the desire
and/or need for information can be overwhelming and, due to time
constraints, commitments, and other limitations (e.g., putting a
task off and forgetting about it), people might not have time to
perform all the research or obtain all the desired information.
Many people utilize avatars as a way to express a computer
generated representation of themselves or as their alter ego. The
avatar can be represented as a two-dimensional icon (e.g., picture)
or a three-dimensional model. Generally, avatars are constructed to
represent a friend or assistant who can interact with the user or
an environment of the user as a distinct entity in relation to the
perspective of that user. However, today's avatars are arbitrarily
discussed or implemented as distinct third-person entities. Once
implemented these avatars generally do nothing more than visually
represent an entity.
SUMMARY
The following presents a simplified summary in order to provide a
basic understanding of some aspects of the disclosed examples. This
summary is not an extensive overview and is intended to neither
identify key or critical elements nor delineate the scope of such
aspects. Its purpose is to present some concepts in a simplified
form as a prelude to the more detailed description that is
presented later.
In accordance with one or more examples and corresponding
disclosure thereof, various aspects are described in connection
with a cognitive agent that acts as a surrogate for a user. The
cognitive agent can autonomously perform actions with little, if
any, interaction from the user. A request for a result can be
provided through an express request or based on an inference.
Actions to achieve the result can be automatically performed and
after completion (or based on other factors) the results can be
presented to the user. While the actions are being autonomously
performed by the cognitive agent, the user is free to perform other
actions that might supplement the actions of cognitive agent and/or
actions that might relate to something else (e.g., traveling,
sleeping, and so on).
In accordance with some aspects, the cognitive agent can be
represented as an avatar. The avatar can assist the user to
complete a task, can provide a guided tour, or perform other
functions (e.g., navigate the user through a store, provide a
workout buddy, and so on). In some aspects, the avatar might only
be perceivable by the user that requested the guidance or other
action from the avatar. However, some avatars can be perceived by
other people within the environment.
According to a further aspect, a cognitive agent can selectively
perform a self-evaluation, such as on machinery, and automatically
implement actions related to the self-evaluation. For example, when
associated with machinery, the cognitive agent can gather
information, such as maintenance records, length in service, known
problems with similar machinery, and so forth. Based on the
gathered information and self-diagnostics, a determination can be
made automatically that parts should be ordered to mitigate an
amount of down time. The cognitive agent can order the parts and
provide instructions on how best to repair and/or perform
preventive maintenance on the machinery.
To the accomplishment of the foregoing and related ends, one or
more examples comprise the features hereinafter fully described and
particularly pointed out in the claims. The following description
and the annexed drawings set forth in detail certain illustrative
aspects and are indicative of but a few of the various ways in
which the principles of the various aspects may be employed. Other
advantages and novel features will become apparent from the
following detailed description when considered in conjunction with
the drawings and the disclosed examples are intended to include all
such aspects and their equivalents.
BRIEF DESCRIPTION OF THE DRAWINGS
FIG. 1 illustrates a system for providing a cognitive agent that
operates as a surrogate by performing various functions without
user intervention.
FIG. 2 illustrates a system that automatically performs one or more
activities as a surrogate for a user.
FIG. 3 illustrates a system for providing information to a user
through implementation of a cognitive agent represented as a
virtual avatar, according to an aspect.
FIG. 4 illustrates a system that is configured to perform
self-diagnostics though a cognitive agent, according to an
aspect.
FIG. 5 illustrates a system that employs machine learning and
reasoning to automate one or more features in accordance with the
disclosed aspects.
FIG. 6 illustrates a method for automatically performing actions to
supplement actions performed by a user, according to an aspect.
FIG. 7 illustrates a method for automatic diagnostics and repair in
accordance with one or more aspects.
FIG. 8 illustrates a block diagram of a computer operable to
execute the disclosed architecture.
FIG. 9 illustrates a schematic block diagram of an exemplary
computing environment in accordance with the various aspects.
DETAILED DESCRIPTION
Various aspects are now described with reference to the drawings.
In the following description, for purposes of explanation, numerous
specific details are set forth in order to provide a thorough
understanding of one or more aspects. It may be evident, however,
that the various aspects may be practiced without these specific
details. In other instances, well-known structures and devices are
shown in block diagram form in order to facilitate describing these
aspects.
As used in this application, the terms "component", "module",
"system", and the like are intended to refer to a computer-related
entity, either hardware, a combination of hardware and software,
software, or software in execution. For example, a component may
be, but is not limited to being, a process running on a processor,
a processor, an object, an executable, a thread of execution, a
program, and/or a computer. By way of illustration, both an
application running on a server and the server can be a component.
One or more components may reside within a process and/or thread of
execution and a component may be localized on one computer and/or
distributed between two or more computers.
Computing systems employing automated learning and reasoning
procedures (e.g., the use of explicitly and/or implicitly trained
statistical classifiers) can be employed in connection with
performing inference and/or probabilistic determinations and/or
statistical-based determinations as in accordance with one or more
aspects as described hereinafter. As used herein, the term
"inference" refers generally to the process of reasoning about or
inferring states of the system, environment, and/or user from a set
of observations as captured through events, sensors, and/or data.
Inference can be employed to identify a specific context or action,
or can generate a probability distribution over states, for
example. The inference can be probabilistic--that is, the
computation of a probability distribution over states of interest
based on a consideration of data and events. Inference can also
refer to techniques employed for composing higher-level events from
a set of events and/or data. Such inference results in the
construction of new events or actions from a set of observed events
and/or stored event data, whether or not the events are correlated
in close temporal proximity, and whether the events and data come
from one or several event and data sources. Various classification
schemes and/or systems (e.g., support vector machines, neural
networks, logic-centric production systems, Bayesian belief
networks, fuzzy logic, data fusion engines, and so on) can be
employed in connection with performing automatic and/or inferred
action in connection with the disclosed aspects.
Various aspects will be presented in terms of systems that may
include a number of components, modules, and the like. It is to be
understood and appreciated that the various systems may include
additional components, modules, and the like and/or may not include
all of the components, modules, and the like discussed in
connection with the figures. A combination of these approaches may
also be used. The various aspects disclosed herein can be performed
on electrical devices including devices that utilize touch screen
display technologies, mouse-and-keyboard type interfaces, cameras,
accelerometers, compass, microphone, barometer, force sensor,
temperature, blood pressure/heart monitoring, and/or other
interfaces. Examples of such devices include computers (desktop and
mobile), smart phones, personal digital assistants (PDAs), and
other electronic devices both wired and wireless.
Additionally, in the subject description, the word "exemplary" is
used to mean serving as an example, instance, or illustration. Any
aspect or design described herein as "exemplary" is not necessarily
to be construed as preferred or advantageous over other aspects or
designs. Rather, use of the word exemplary is intended to present
concepts in a concrete fashion.
Referring initially to FIG. 1, illustrated is a system 100 for
providing a cognitive agent that operates as a surrogate by
performing various functions without (or with minimal) user
intervention. The cognitive agent can be a machine assistant that
automatically starts an action and/or completes an action. In
accordance with some aspects, the cognitive agent provides enough
information to allow a human (e.g., personal assistant) to perform
an action (e.g., receives an input from a user and transmits the
appropriate information to a human assistant).
System 100 includes a receiver component 102 that is configured to
accept a request 104 for performance of a function that achieves an
end result. The request 104 can be received directly from the user,
such as though a direct input (e.g., the user typing in the request
on a keyboard, the user verbally stating the request, and so on).
In accordance with some aspects, the request can be based on an
inference that the user would like a particular function to be
performed in order for a result to be achieved. The inference can
be made based on observance of a different activity being performed
by the user, no activities performed by the user after receipt of
the request, or based on other considerations (e.g., the user is
traveling, calendar indicates the user is not available, and so
forth).
A cognitive agent component 106 is configured to perform the
requested activity automatically. For example, cognitive agent
component 106 can collect data related to a topic that the user is
researching and parse the data for various content included
therein. In another example, cognitive agent component 106 can
anticipate a user request and, prior to receiving the request,
perform one or more actions that can satisfy the request.
In another example, cognitive agent component 106 can perform
mapping operations that can be utilized to benefit a user and/or
respond to a user request. For example, a user might be searching
for a particular item (e.g., a restaurant) and system 100 can
automatically attempt to find more of the same type of item (e.g.,
restaurants of a same type, such as Italian or Mexican
restaurants).
In a further example, based on a profile of the user, gathered
through historical data analysis, cognitive agent component 106 can
enhance a user experience, such as by offering related information.
For example, if a request is received for a general keyword search,
system 100 can attempt to understand further context related to the
query and determine what is related to the initial search terms.
Thus, instead of strictly solving the relevance problem in terms of
providing a plurality of results sorted by best match, system 100
can instead provide other items that the user might not have
considered (e.g., a search for Rome history is received and system
100 suggests Greek history information also). The other items can
be determined based on previous searches, searches performed by
other users, synonyms, or other criteria.
It should be understood that the gathering of information related
to the user and/or other users can take into account various
privacy concerns. For example, a user might need to specifically
allow system 100 to collect information associated with that user
before any monitoring and/or collection is performed. According the
some aspects, the user might need to periodically (e.g., every few
months, every year, and so forth) reconfirm that information can be
gathered by system 100. If the user does not respond to a prompt
asking if the information can be gathered (or if the user denies
the request), gathering by system 100 is not performed.
In accordance with some aspects, cognitive agent component 106 can
include functionality related to recognition of related activities
that should be carried out autonomously in order to provide the
user with accurate and complete results. The recognition
functionality can review search results, for example, in order to
discover commonalities between the results, differences between
subsets of results, or anomalies (e.g., one result is different
than the other results). In accordance with some aspects, search
results might be disregarded based on various criteria, such as
dates (e.g., if older than three months ignore), authorship,
language, and so forth.
For example, a user might be interested in purchasing a piece of
real estate and would like information about offers for sale in a
certain geographic location. Cognitive agent component 106 can
automatically find real estate that meets criteria pre-defined by
the user (e.g., three bedrooms, a pool, and a four car garage).
Cognitive agent component 106 can review the content associated
with the discovered real estate and might determine that there are
gas wells and oil wells on the property. Cognitive agent component
106 can go beyond the mere search for real estate and can review
information associated with mineral rights, royalty rights, oil and
gas leases, and so forth. In accordance with some aspects,
cognitive agent component 106 can access government databases, such
as a natural resources department, to determine the production
rates of the oil wells and/or gas wells so that the user has
necessary information in order to make an informed decision,
without having to specifically request this information.
The results of the requested activity are rendered by an output
component 108. The results can be rendered in any perceivable
format (e.g., visual, audio, tactile, and so forth). Alternatively
or additionally, the results rendered can conform to various
parameters (e.g., device through which the results will be
presented, location of the user, preferences of user, and so
forth).
In accordance with some aspects, the gathered information can be
condensed (or expanded) prior to outputting the information based
on display screen constraints and/or other device parameters. For
example, cognitive agent component 106 can sort the gathered
information based on a topic associated with each piece of gathered
information. The user can be provided a link to a full version of
the information. Thus, the user can browse the condensed version
and, if a particular item is of interest, the user can select the
link and the full version of that item can be presented to the
user. In another example, the user can browse the expanded version
of information and condense (or remove from view) at least a subset
of information if it is not of interest.
System 100 can be further configured to compliment a user as the
user works, talks with others, and performs other actions. Receiver
component 102 can recover data from the various user actions and/or
the actions of others that are related to the user (e.g., others
that are talking to the user). Based on these actions, cognitive
agent component 106 can compile relevant data, such as gathering
additional content related to the user's current conversational
subject. The relevant data can be rendered by output component 108
in various formats (e.g., visual, on a screen, in a head-on
display, by audio through an ear piece, tactile, and so on).
FIG. 2 illustrates a system 200 that automatically performs one or
more activities as a surrogate for a user. Included in system 200
is a receiver component 202 that is configured to receive a request
for implementation of an activity (to achieve a result) by a
cognitive agent component 206. The result of the one or more
activities can be presented to the user by an output component
208.
In further detail, a user request can be an explicit request 210
and/or an inferred request 212 (e.g., an automatic request). The
explicit request 210 can input by the user through interaction with
an interface component 214. For example, a user might be running
late to a meeting but needs certain information for that meeting
(or for after the meeting). As the user is leaving for the meeting,
he can enter three or four key words into a search query of
interface component 214.
The interface component 214 can provide a graphical user interface
(GUI), a command line interface, a speech interface, Natural
Language text interface, and the like. For example, a GUI can be
rendered that provides a user with a region or means to load,
import, select, read, and so forth, various requests and can
include a region to present the results of such. These regions can
comprise known text and/or graphic regions comprising dialogue
boxes, static controls, drop-down-menus, list boxes, pop-up menus,
as edit controls, combo boxes, radio buttons, check boxes, push
buttons, and graphic boxes. In addition, utilities to facilitate
the information conveyance such as vertical and/or horizontal
scroll bars for navigation and toolbar buttons to determine whether
a region will be viewable can be employed. Thus, it might be
inferred that the user did want the action performed.
The user can also interact with the regions to select and provide
information through various devices such as a mouse, a roller ball,
a keypad, a keyboard, a pen, gestures captured with a camera,
and/or voice activation, for example. Typically, a mechanism such
as a push button or the enter key on the keyboard can be employed
subsequent to entering the information in order to initiate
information conveyance. However, it is to be appreciated that the
disclosed aspects are not so limited. For example, merely
highlighting a check box can initiate information conveyance. In
another example, a command line interface can be employed. For
example, the command line interface can prompt the user for
information by providing a text message, producing an audio tone,
or the like. The user can then provide suitable information, such
as alphanumeric input corresponding to an option provided in the
interface prompt or an answer to a question posed in the prompt. It
is to be appreciated that the command line interface can be
employed in connection with a GUI and/or API. In addition, the
command line interface can be employed in connection with hardware
(e.g., video cards) and/or displays (e.g., black and white, and
EGA) with limited graphic support, and/or low bandwidth
communication channels.
In accordance with some aspects, an observation component 216 can
be utilized to analyze user actions and determine whether a request
should be inferred 212 based on a user action and/or inaction. For
example, the user might type in (or speak) a few words before
leaving for a meeting. However, the user forgot to press an enter
key (or perform another action to initiate information conveyance).
In this situation, observation component 216 would observe the user
entering the key words and then leaving, without completing the
action (e.g., information conveyance). In accordance with some
aspects, a change of user condition can be utilized as a base for
operations. For example, the entrance of a new person into a room
can initiate retrieval of information related to the person (e.g.,
his name, the fact that he owes the user money, a paper authorized
by the person, and so on).
Cognitive agent component 206 can operate as a surrogate for the
requestor by performing various functions, such as gathering
relevant information, researching a topic, and/or other labor
intensive functions that a user might not have time (or a desire)
to perform. As such, the cognitive agent component 206 can perform
research and/or gather information while the requestor is
performing a different task (e.g., driving to work, attending a
meeting, performing a different computer task, and so forth). When
the requestor is ready for the information (or at another
appropriate time), the cognitive agent component 206 causes the
results of the activities to be rendered by output component 208.
In accordance with some aspects, output component 208 can present
to the requestor gathered information, simply provide an indication
that the function is completed, or other data that allows requestor
to verify that the activities are complete. In accordance with some
aspects, the information presented by output component 208 can
indicate that a particular function cannot be completed or can only
be completed partially.
In order to carry out the various activities, cognitive agent
component 206 can interact with an association component 218 that
can gather and retain information related to the requestor. For
example, information gathered by association component 218 can
relate to user preferences 220, historical data 222, predefined
parameters 224, or combinations thereof. The user preferences 220
can relate to how to present results (e.g., in a visual format, in
an audible format, and so on), when to implement actions
automatically (e.g., when the user is not utilizing the device, at
predefined times, or based on other criteria), and so on.
The historical data 222 can relate to actions that have been
previously requested and/or based on other activities of the user.
The predefined parameters 224 can relate to criteria that should be
met (or that should not be met) related to implementation of
actions to achieve a result. In an example, the user might search
for Mexican restaurants in a map application. During the same
session, the user might next search for stereo stores. Historical
data 222 related to these two searches (Mexican restaurants and
stereo stores) can be retained in a user profile. However, even
though the two different searches were performed in the same
session, each search can be retained separately as distinct
historical data 222. Then, the next time the user searches for
restaurants, the Mexican restaurant profile is obtained and results
related to Mexican restaurants are automatically returned, since
the historical data 222 indicates that the user is interested in
Mexican restaurants. However, information related to stereo stores
is not returned, since it is distinguished from the restaurant
request.
In an example, a user can submit a request that various documents
be gathered (e.g., from a database, from multiple databases, over
the Internet, and so on) while the user travels to a meeting. While
traveling to that meeting, the cognitive agent component 206
receives the request and infers that the user would like the
information upon arriving at a destination (such as by observing
that the user is traveling (e.g., GPS data)). Based on the request,
cognitive agent component 206 performs the search and/or gathers
the information so that the user does not waste precious time
performing the search (and be even later to the meeting). If there
is a question related to the query and/or the type of information
to gather, the cognitive agent can contact the user (e.g., through
a mobile phone, text message, so on) for further information.
In accordance with some aspects, a condition of the user can
control the level of updates and/or prompts for
clarification/additional information. For example, when the user is
sleeping (or during the user's normal sleep time, based on
historical observation), the user is interrupted only for an
emergency. When the user is operating a vehicle, interruptions are
limited. In another example, when the user is speaking, only
additional data relevant for the subject of the conversation is
provided at intervals during which the user is not speaking.
Output component 208 can render the result of the activities in any
perceivable format (e.g., visual, audio, tactile, and so on). In
accordance with some aspects, the perceivable format can be in a
manner that facilitates usage of the information based on a current
user context, which can be determined by a context component 226.
For example, context component 226 can ascertain the type of device
through which the results will be presented and selectively alter
the manner of outputting the data to conform to the device.
In accordance with some aspects, context component 226 can evaluate
the user environment prior to rendering the results, which can be
useful if the information is personal, confidential, or has a
sensitivity level above a threshold (which can be predefined by the
user or established based on analysis of the information to be
rendered). For example, if the user is in a noisy situation, as
determined by context component 226, the information (e.g., search
results, map data such as driving directions) can be output in a
visual manner. In another example, if the user is in a situation
where viewing a display would be difficult (e.g., direct sunlight)
the results can be automatically output in another format, such as
audibly through speakers.
FIG. 3 illustrates a system 300 for providing information to a user
through implementation of a cognitive agent represented as a
virtual avatar, according to an aspect. An avatar is generally a
computer generated representation of a person's representation of
himself/herself or an alter ego. The avatar can be represented as a
two-dimensional icon (e.g., picture) or a three-dimensional model.
In accordance with some aspects, the avatar can be an image based
rendering, such as morphing a video to simulate talking
movements.
System 300 includes a receiver component 302 that obtains (from a
user 304 and/or automatically though an inference of what user 304
might want) an explicit request or an inferred request for one or
more actions to be implemented without the user's intervention. In
accordance with this aspect, the request can be for a virtual
representation of another (in the form of an avatar) to be
rendered, which can allow the requestor to visually obtain
information in real-time with little, if any, interaction by user.
Cognitive agent component 306 can gather information related to one
or more avatars 310 and at least one of the avatars 310 can be
rendered by output component 308.
As illustrated, receiver component 302 and output component 308 can
be included in a user interface component 312. User interface
component 312 is intended to include or manage all or a portion of
input/output operations associated with user 304 and/or otherwise
described herein.
Output component 308 and/or user interface component 312 can
include a projector that can display at least a portion of an
avatar 310 in a volume of physical space that substantially
encapsulates at least a portion of target user 304 or that appears
to encapsulate a portion of target user 304. That is to say, a
projector can be utilized to initially project or display avatar
310 on or over potions of user 304, which can facilitate a
perspective or experience in connection with avatar 310.
A projector can be one or more of a laser-based projector, a light
emitting diode (LED) projector or another type of projector,
including a virtual retinal display (VRD)-based projector. User
interface component 312 can also include one or more cameras (e.g.,
to monitor movements, behaviors, features, and/or context in
connection with user 304, avatar 310, and/or a local environment
314). Also, user interface component 312 can include one or more
speakers to present audio outputs.
Further, user interface component 312 can include one or more
displays (e.g., monitor, touch-screen, multi-touch surface, head up
display (HUD), stereo or Auto-stereo screen, automatic virtual
environments, retinal projection, a virtual retinal display, and so
forth). In accordance with some aspects, avatar 310 can be visible
when viewed through a HUD or VRD. Accordingly, avatar 310 might not
be visible to the naked eye, which can be dependent upon the
implementation details or equipment employed by user 304. Further,
user interface component 312 can include one or more network
adapters to provide network accessibility. Although not strictly
necessary, network adapter can typically relate to a wireless
network.
Alternatively or additionally, one or more keyboards or keypads
(standard keys or buttons as well as soft or virtual keys or
buttons) can be included in user interface component 312. Also
included can be one or more microphones and one or more
accelerometers to monitor motion or mechanical accelerations of all
or portions of user 304. Further, a variety of other input/output
components or sensors can be included in user interface component
312. Such elements include, for example, biometric sensors (e.g.,
heart rate, blood pressure, and so on), gaze-tracking sensors, or
substantially any other suitable input/output component or
sensor.
In an example, a trip to France is being planned and a requester
304 desires to know places to visit while on the trip. A mapping
application that provides street-side imaging can be presented that
allows for viewing of the location and places within that location.
The cognitive agent 306 can search for the best places and create
an itinerary of events that can be performed during the trip.
In another example, while physically in France, an avatar 310 of a
friend that previously visited France can be presented (by output
component 308) and can demonstrate the sequence of places she
visited and/or recommends. In such a manner, the requestor can
virtually see the avatar of the friend while traversing the streets
in France. Thus, the avatar 310 can be a virtual guide, which can
be beneficial when visiting an unfamiliar location. In accordance
with some aspects, cognitive agent component 306, or another system
300 component, can translate local language to a language
understandable by the user, and vice versa.
Output component 308 can configure the rending of the avatar to
conform with the requestors current conditions. For example, if the
user is in a vehicle, the output can be presented on a heads-up
display (e.g., overlaid on a portion of a windshield, displayed on
a portion of a rear-view mirror, or on another device that allows
the requestor to visualize the avatar without taking her eyes from
the road). In another example, if the requestor is walking, the
avatar can be presented on a mobile device (e.g., mobile phone,
heard in an ear piece). In accordance with some aspects, output
component 308 can selectively change the manner of presenting the
avatar. For example, the requestor is viewing the avatar in a car
and the avatar is displayed on a portion of the windshield. When
the vehicle is no longer in motion (e.g., parked, stopped at a red
light, pulled over to the side of the road, and so on), a full
display image of the avatar might be shown in order to enhance the
user experience.
In accordance with some aspects, the avatar or other virtual
representation can move with the requestor as the requestor is
traveling. For example, the requestor might be in a store, such a
home improvement store, and desires to build a deck. The requestor
might not know what is needed to build the deck, but has a
schematic representation with dimensions and other necessary
information to customize the deck. Other people (that can be in a
remote location (e.g., not in the store)) can be searched for to
determine if anyone else has built a deck and has ideas or hints
that they can provide to assist the requestor. The person
responding to the request can be considered an expert in the field
or domain of the desired task.
As the requestor travels through the store, there can be surfaces
on which the avatar can be displayed (e.g., at every aisle) or a
user device can project the avatar image such that only the
requestor can view the image and/or hear the image in the form of
an audio output. The avatar can help the user navigate the store
or, in another example, arrows can visually display on the floor
pointing to the correct direction to find the paint or the wood
stain or other items that would be necessary for the deck. For
example, the arrow can point to the left or the right or straight
ahead. The information displayed can be a live feed, either
text-based, audio-based, video-based, or combinations thereof; or
previously recorded.
Alternatively or additionally, system 300 can include or be
operatively connected to a data store 316. Data store 316 is
intended to be a repository of all or portions of data, data sets,
or information described herein or otherwise suitable for use with
the disclosed aspects. Data store 316 can be centralized, either
remotely or locally cached, or distributed, potentially across
multiple devices and/or schemas. Furthermore, data store 316 can be
embodied as substantially any type of memory, including but not
limited to volatile or non-volatile, sequential access, structured
access, or random access and so on. It should be understood that
all or portions of data store 316 can be included in system 300, or
can reside in part or entirely remotely from system 300.
In another example, many people work out for health reasons and
personal reasons. However, some people might not be motivated to
work out alone and/or would have more incentive to work out if
competing with another. Thus, a person can be on a treadmill, for
example, and search for another person (in the form of an avatar)
that is willing to run with them. In accordance with some aspects,
the ability of the avatar can be adjusted such that the ability of
the avatar can more closely match the requestor. In such a manner,
the speed of the avatar 310 can be set or adjusted, either by the
user 304 or automatically based upon an inference or intelligent
determination. For example, avatar actions can be sped up or slowed
down according to a user's competence or comfort level or an
inference thereof.
Alternatively or additionally, real-world experiences can be
applied, such as inclinations, differences in terrain, and so
forth. In accordance with some aspects, a map event can be
included, such as running with a marathon, which can be helpful if
the requestor is training for a race or other competition events.
These aspects can also be applied to other sporting events (e.g.,
bike riding, swimming, and so forth).
With reference now to FIG. 4, illustrated is a system 400 that is
configured to perform self diagnostics though a cognitive agent,
according to an aspect. System 400 can be configured to diagnose a
problem and/or attempt to mitigate machinery failures
automatically. A receiver component 402 can be associated with
machinery, such as industrial machines, manufacturing machines, and
so forth. The receiver component 402 can gather data from various
machine components through the use of sensors or other data (e.g.,
cycle counts, noise or decibel levels, and so on).
A cognitive agent component 406 can intelligently gather
information related to the machinery. The information can include
operating parameters, tolerance limits for machinery parts (e.g.,
wear items), expected life for wearable machinery parts, as well as
other factors associated with the machinery (e.g., maintenance
history, preventive maintenance schedule, and so on). Additionally,
information related to a serial number, date of manufacture, name
of (and other information associated with) a manufacturer, name of
(and other information associated with) a parts supplier and/or
service technician.
As a function of the information gathered and/or retained by
cognitive agent component 406, a diagnostics component 410 can
perform self-diagnostics on the machinery. The diagnostics can be
performed at regular intervals, when a problem is suspected, or
based on other intervals (e.g., idle time, planned shutdown,
unplanned shut down, and so forth). The diagnostics can measure
various parameters and gather pertinent data (e.g., cycle times,
processing parameters, and so on) and report the information to
cognitive agent component 406.
The information can be utilized by cognitive agent component 406 to
determine whether there is a potential problem with the machinery
(e.g., parts should be replaced before a catastrophic failure
occurs and/or to preserve the life of the machinery, and so on). In
accordance with some aspects, cognitive agent component 406 can
gather the information in order to evaluate machinery reliability,
quality, and/or other factors that can be considered for a future
purchasing decision (e.g., what is the mean time between failures
for this machine, how does this machine manufacturer compare to
other manufacturers, and so on).
Alternatively or additionally, if a problem is discovered by
diagnostics component 410, cognitive agent component 406 can
automatically take action to correct the problem. For example, it
might be determined that a part should be replaced. Cognitive agent
component 406 can access the manufacture part number and
autonomously order the part (based on pre-established criteria
related to pricing, delivery, payment terms, and so on). In
accordance with some aspects, cognitive agent component 406 can
gather data related to costs associated with purchasing a
replacement part and might even compare those costs with the
estimated cost of not buying the part (e.g., what can be expected
by not taking action now). Further, cognitive agent component 406
can review relevant information and make a determination whether
action should be taken immediately or if there is adequate time
before action is necessary (e.g., days, weeks, months, and so
on).
According to some aspects, cognitive agent component 406 might
research a detected issue before presenting the results of the
diagnostic test. For example, cognitive agent component 406 might
access the manufacturer's website for information related to the
machinery. In another example, cognitive agent component 406 can
access an operation and maintenance manual for the needed
information.
Data associated with the actions and/or recommendations of
cognitive agent component 406 can be provided through output
component 408. For example, after a part is ordered, output
component 408 can automatically convey the purchase information
(e.g., invoice, shipping details, and so on) to a user and/or
entity responsible for paying the invoice, receiving the part, and
so forth.
FIG. 5 illustrates a system 500 that employs machine learning and
reasoning to automate one or more features in accordance with the
disclosed aspects. Included in system 500 is a receiver component
502 that is configured to receive a request from a user and/or
entity (e.g., the Internet, another system, a computer, machinery,
and so forth), hereinafter referred to as users and/or entity,
depending on the context. The request can be an explicit request or
an implicit request (which can be an inferred request). A cognitive
agent component 506 is configured to interpret the request and
determine actions that need to be conformed to in order to comply
with the request. In accordance with some aspects, the
determination might be that actions by other people and/or entities
need to be performed to comply with the request. Details related to
the actions implemented by cognitive agent component 506 are
rendered by output component 508. According to some aspects,
cognitive agent component 506 might not be able to process the
request, thus, a failure message can be conveyed through output
component 508. The failure message can include reasons why the
request could not be completed. The reason might be that more
information is needed and/or other people/entities that are needed
to complete the request are not available and/or unable to
assist.
A machine learning component 510 can employ various machine
learning techniques to automatic one or more features. The machine
learning and reasoning component 510 can employ principles of
probabilistic and decision theoretic inference and rely on
predictive models constructed through the use of machine learning
procedures. Logic-centric inference can also be employed separately
or in conjunction with probabilistic methods. The machine learning
and reasoning component 510 can infer intention of a request by
obtaining knowledge about the possible actions and knowledge about
what the user is trying to accomplish based on applications or
programming being implemented by the user, the application/program
context, the user context, or combinations thereof. Based on this
knowledge, the machine learning and reasoning component 510 can
make an inference based on which actions to implement, which other
users/entities to employ, or combinations thereof.
If machine learning and reasoning component 510 has uncertainty
related to the intent or request, machine learning and reasoning
component 510 can automatically engage in a short (or long)
dialogue or interaction with the user (e.g., "What do you mean?").
In accordance with some aspects, machine learning component 510
engages in the dialogue with the user through another system
component. Computations of the value of information can be employed
to drive the asking of questions. Alternatively or additionally,
cognitive agent component 506 can anticipate a user action (e.g.,
"where is he heading to?") and continually update a hypothesis as
more user actions are gathered. Cognitive agent component 506 can
accumulate data or perform other actions that are a result of
anticipation of the user's future actions.
The various aspects (e.g., in connection with receiving a request,
determining the meaning of the request, distinguishing a request
from other actions, implementation of actions to satisfy the
request, and so forth) can employ various artificial
intelligence-based schemes for carrying out various aspects
thereof. For example, a process for determining if a particular
action is a request for an action to be performed or a general
action (e.g., an action that the user desires to perform manually)
can be enabled through an automatic classifier system and
process.
A classifier is a function that maps an input attribute vector,
x=(x1, x2, x3, x4, xn), to a confidence that the input belongs to a
class, that is, f(x)=confidence(class). Such classification can
employ a probabilistic and/or statistical-based analysis (e.g.,
factoring into the analysis utilities and costs) to prognose or
infer an action that a user desires to be automatically performed.
In the case of requests, for example, attributes can be common
requests, a combination of requests, a pattern of requests, and the
classes are applications or functions that need to be utilized to
satisfy the request.
A support vector machine (SVM) is an example of a classifier that
can be employed. The SVM operates by finding a hypersurface in the
space of possible inputs, which hypersurface attempts to split the
triggering criteria from the non-triggering events. Intuitively,
this makes the classification correct for testing data that is
near, but not identical to training data. Other directed and
undirected model classification approaches include, for example,
naive Bayes, Bayesian networks, decision trees, neural networks,
fuzzy logic models, and probabilistic classification models
providing different patterns of independence can be employed.
Classification as used herein also is inclusive of statistical
regression that is utilized to develop models of priority.
As will be readily appreciated from the subject specification, the
one or more aspects can employ classifiers that are explicitly
trained (e.g., through a generic training data) as well as
implicitly trained (e.g., by observing user behavior, receiving
extrinsic information). For example, SVM's are configured through a
learning or training phase within a classifier constructor and
feature selection module. Thus, the classifier(s) can be used to
automatically learn and perform a number of functions, including
but not limited to determining according to a predetermined
criteria when to implement an action, which action to implement,
what requests to group together, relationships between requests,
and so forth. The criteria can include, but is not limited to,
similar requests, historical information, and so forth.
Additionally or alternatively, an implementation scheme (e.g.,
rule) can be applied to control and/or regulate requests and
resulting actions, inclusion of a group of users to carry out
actions associated with the requests, privileges, and so forth. It
will be appreciated that the rules-based implementation can
automatically and/or dynamically interpret a requests based upon a
predefined criterion. In response thereto, the rule-based
implementation can automatically interpret and carry out functions
associated with the request by employing a predefined and/or
programmed rule(s) based upon any desired criteria.
In view of the exemplary systems shown and described above,
methodologies that may be implemented in accordance with the
disclosed subject matter, will be better appreciated with reference
to the following flow charts. While, for purposes of simplicity of
explanation, the methodologies are shown and described as a series
of blocks, it is to be understood and appreciated that the
disclosed aspects are not limited by the number or order of blocks,
as some blocks may occur in different orders and/or at
substantially the same time with other blocks from what is depicted
and described herein. Moreover, not all illustrated blocks may be
required to implement the methodologies described hereinafter. It
is to be appreciated that the functionality associated with the
blocks may be implemented by software, hardware, a combination
thereof or any other suitable means (e.g. device, system, process,
component). Additionally, it should be further appreciated that the
methodologies disclosed hereinafter and throughout this
specification are capable of being stored on an article of
manufacture to facilitate transporting and transferring such
methodologies to various devices. Those skilled in the art will
understand and appreciate that a methodology could alternatively be
represented as a series of interrelated states or events, such as
in a state diagram.
FIG. 6 illustrates a method 600 for automatically performing
actions to supplement other actions performed by a user. Method 600
starts, at 602, when a request for performance of an action is
received. The request can be a specific request or an inferred
request and can relate to searches, mapping applications, or other
actions to be performed. The performance of an action can be
intended to be performed automatically while the requestor is
performing another action at substantially the same time. In
accordance with some aspects, the request is inferred based upon
observance of another activity being performed or no activities
performed after receipt of the request. In accordance with some
aspects, the action can supplement an activity of the user.
At 604, the requested information is gathered and analyzed. The
information can be gathered as a function of preferences,
historical information, predefined parameters, or combinations
thereof. The gathered information is presented, at 606. The
presentation is in a manner that facilitates usage of the
information based on a current user context, which can be device
parameters, an environment, an activity level (e.g., is the user
performing another task), or combinations thereof. In accordance
with some aspects, the gathered information can be presented in the
form of a virtual avatar.
In accordance with some aspects, the gathered information is
condensed based on a topic associated with each piece of gathered
information and the condensed information is presented with a link
to a full version of the information. According to some aspects, a
subsequent action is performed as a function of the gathered
information and linkage data for the gathered information and the
subsequent action is provided to a user.
According to some aspects, the gathered and presented data can
relate to a user search based on known information about the user.
For example, the user might have children. As the user is searching
for hotels in a specific area of a map application, the user might
be interested in hotels that offer continental breakfasts. However,
based on the knowledge that the user has children, results that do
not necessarily conform to the continental breakfast requirement
might be presented to the user if those hotels are advertised as
family friendly, as this might be more useful for the user. These
alternative search results might be presented in a format that lets
the user know there are alternatives available and if the user is
interested, those alternatives will be presented. Thus, the user is
given the option to obtain the original search results and/or the
expanded search results.
FIG. 7 illustrates a method 700 for automatic diagnostics and
repair in accordance with one or more aspects. Method 700 can be
implemented to mitigate machinery failure and prolong the life of
the machinery. Method 700 starts, at 702, when data related to a
machine is gathered (e.g., cycle counts, noise or decibel levels,
and so on.
At 704, machine information is accessed and a determination is made
whether testing of the machine should occur. The machine
information can include operating parameters, tolerance limits for
machinery parts, expected life for wearable machinery parts, as
well as other factors associated with the machinery (e.g.,
maintenance history, preventive maintenance schedule, and so on).
Additionally, information related to a serial number, date of
manufacture, name of (and other information associated with) a
manufacturer, name of (and other information associated with) a
parts supplier and/or service technician.
An instruction to perform a test on the machinery is sent and the
results of the test are output to a user. The results of the test
can include the actual test report as well as subsequent actions
that were performed based on the test results. The diagnostics can
be performed at regular intervals, when a problem is suspected, or
based on other intervals (e.g., idle time, unplanned shut down,
planned shut down, and so forth). The diagnostics can measure
various parameters and gather pertinent data (e.g., cycle times,
processing parameters, and so on). The diagnostics can result in
actions that are performed automatically and/or actions that are
recommended to be performed by a user.
Referring now to FIG. 8, there is illustrated a block diagram of a
computer operable to execute the disclosed architecture. In order
to provide additional context for various aspects disclosed herein,
FIG. 8 and the following discussion are intended to provide a
brief, general description of a suitable computing environment 800
in which the various aspects can be implemented. While the one or
more aspects have been described above in the general context of
computer-executable instructions that may run on one or more
computers, those skilled in the art will recognize that the various
aspects also can be implemented in combination with other program
modules and/or as a combination of hardware and software.
Generally, program modules include routines, programs, components,
data structures, etc., that perform particular tasks or implement
particular abstract data types. Moreover, those skilled in the art
will appreciate that the inventive methods can be practiced with
other computer system configurations, including single-processor or
multiprocessor computer systems, minicomputers, mainframe
computers, as well as personal computers, hand-held computing
devices, microprocessor-based or programmable consumer electronics,
and the like, each of which can be operatively coupled to one or
more associated devices.
The illustrated aspects may also be practiced in distributed
computing environments where certain tasks are performed by remote
processing devices that are linked through a communications
network. In a distributed computing environment, program modules
can be located in both local and remote memory storage devices.
A computer typically includes a variety of computer readable media.
Computer readable media can be any available media that can be
accessed by the computer and includes both volatile and nonvolatile
media, removable and non-removable media. By way of example, and
not limitation, computer-readable media can comprise computer
storage media and communication media. Computer storage media
includes both volatile and nonvolatile, removable and non-removable
media implemented in any method or technology for storage of
information such as computer-readable instructions, data
structures, program modules or other data. Computer storage media
includes, but is not limited to, RAM, ROM, EEPROM, flash memory or
other memory technology, CD-ROM, digital video disk (DVD) or other
optical disk 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.
Communication media typically embodies 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 information delivery media. The term
"modulated data signal" means a signal that has one or more of its
characteristics set or changed in such 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.
With reference again to FIG. 8, the exemplary environment 800 for
implementing various aspects includes a computer 802, the computer
802 including a processing unit 804, a system memory 806 and a
system bus 808. The system bus 808 couples system components
including, but not limited to, the system memory 806 to the
processing unit 804. The processing unit 804 can be any of various
commercially available processors. Dual microprocessors and other
multi-processor architectures may also be employed as the
processing unit 804.
The system bus 808 can be any of several types of bus structure
that may further interconnect to a memory bus (with or without a
memory controller), a peripheral bus, and a local bus using any of
a variety of commercially available bus architectures. The system
memory 806 includes read-only memory (ROM) 810 and random access
memory (RAM) 812. A basic input/output system (BIOS) is stored in a
non-volatile memory 810 such as ROM, EPROM, EEPROM, which BIOS
contains the basic routines that help to transfer information
between elements within the computer 802, such as during start-up.
The RAM 812 can also include a high-speed RAM such as static RAM
for caching data.
The computer 802 further includes an internal hard disk drive (HDD)
814 (e.g., EIDE, SATA), which internal hard disk drive 814 may also
be configured for external use in a suitable chassis (not shown), a
magnetic floppy disk drive (FDD) 816, (e.g., to read from or write
to a removable diskette 818) and an optical disk drive 820, (e.g.,
reading a CD-ROM disk 822 or, to read from or write to other high
capacity optical media such as the DVD). The hard disk drive 814,
magnetic disk drive 816 and optical disk drive 820 can be connected
to the system bus 808 by a hard disk drive interface 824, a
magnetic disk drive interface 826 and an optical drive interface
828, respectively. The interface 824 for external drive
implementations includes at least one or both of Universal Serial
Bus (USB) and IEEE 1394 interface technologies. Other external
drive connection technologies are within contemplation of the one
or more aspects.
The drives and their associated computer-readable media provide
nonvolatile storage of data, data structures, computer-executable
instructions, and so forth. For the computer 802, the drives and
media accommodate the storage of any data in a suitable digital
format. Although the description of computer-readable media above
refers to a HDD, a removable magnetic diskette, and a removable
optical media such as a CD or DVD, it should be appreciated by
those skilled in the art that other types of media which are
readable by a computer, such as zip drives, magnetic cassettes,
flash memory cards, cartridges, and the like, may also be used in
the exemplary operating environment, and further, that any such
media may contain computer-executable instructions for performing
the methods disclosed herein.
A number of program modules can be stored in the drives and RAM
812, including an operating system 830, one or more application
programs 832, other program modules 834 and program data 836. All
or portions of the operating system, applications, modules, and/or
data can also be cached in the RAM 812. It is appreciated that the
various aspects can be implemented with various commercially
available operating systems or combinations of operating
systems.
A user can enter commands and information into the computer 802
through one or more wired/wireless input devices, e.g., a keyboard
838 and a pointing device, such as a mouse 840. Other input devices
(not shown) may include a microphone, an IR remote control, a
joystick, a game pad, a stylus pen, touch screen, or the like.
These and other input devices are often connected to the processing
unit 804 through an input device interface 842 that is coupled to
the system bus 808, but can be connected by other interfaces, such
as a parallel port, an IEEE 1394 serial port, a game port, a USB
port, an IR interface, etc.
A monitor 844 or other type of display device is also connected to
the system bus 808 through an interface, such as a video adapter
846. In addition to the monitor 844, a computer typically includes
other peripheral output devices (not shown), such as speakers,
printers, etc.
The computer 802 may operate in a networked environment using
logical connections through wired and/or wireless communications to
one or more remote computers, such as a remote computer(s) 848. The
remote computer(s) 848 can be a workstation, a server computer, a
router, a personal computer, portable computer,
microprocessor-based entertainment appliance, a peer device or
other common network node, and typically includes many or all of
the elements described relative to the computer 802, although, for
purposes of brevity, only a memory/storage device 850 is
illustrated. The logical connections depicted include
wired/wireless connectivity to a local area network (LAN) 852
and/or larger networks, e.g., a wide area network (WAN) 854. Such
LAN and WAN networking environments are commonplace in offices and
companies, and facilitate enterprise-wide computer networks, such
as intranets, all of which may connect to a global communications
network, e.g., the Internet.
When used in a LAN networking environment, the computer 802 is
connected to the local network 852 through a wired and/or wireless
communication network interface or adapter 856. The adaptor 856 may
facilitate wired or wireless communication to the LAN 852, which
may also include a wireless access point disposed thereon for
communicating with the wireless adaptor 856.
When used in a WAN networking environment, the computer 802 can
include a modem 858, or is connected to a communications server on
the WAN 854, or has other means for establishing communications
over the WAN 854, such as by way of the Internet. The modem 858,
which can be internal or external and a wired or wireless device,
is connected to the system bus 808 through the serial port
interface 842. In a networked environment, program modules depicted
relative to the computer 802, or portions thereof, can be stored in
the remote memory/storage device 850. It will be appreciated that
the network connections shown are exemplary and other means of
establishing a communications link between the computers can be
used.
The computer 802 is operable to communicate with any wireless
devices or entities operatively disposed in wireless communication,
e.g., a printer, scanner, desktop and/or portable computer,
portable data assistant, communications satellite, any piece of
equipment or location associated with a wirelessly detectable tag
(e.g., a kiosk, news stand), and telephone. This includes at least
Wi-Fi and Bluetooth.TM. wireless technologies. Thus, the
communication can be a predefined structure as with a conventional
network or simply an ad hoc communication between at least two
devices.
Wi-Fi, or Wireless Fidelity, allows connection to the Internet from
home, in a hotel room, or at work, without wires. Wi-Fi is a
wireless technology similar to that used in a cell phone that
enables such devices, e.g., computers, to send and receive data
indoors and out; anywhere within the range of a base station. Wi-Fi
networks use radio technologies called IEEE 802.11 (a, b, g, etc.)
to provide secure, reliable, fast wireless connectivity. A Wi-Fi
network can be used to connect computers to each other, to the
Internet, and to wired networks (which use IEEE 802.3 or Ethernet).
Wi-Fi networks operate in the unlicensed 2.4 and 5 GHz radio bands,
at an 11 Mbps (802.11a) or 54 Mbps (802.11b) data rate, for
example, or with products that contain both bands (dual band), so
the networks can provide real-world performance similar to the
basic 10BaseT wired Ethernet networks used in many offices.
Referring now to FIG. 9, there is illustrated a schematic block
diagram of an exemplary computing environment 900 in accordance
with the various aspects. The system 900 includes one or more
client(s) 902. The client(s) 902 can be hardware and/or software
(e.g., threads, processes, computing devices). The client(s) 902
can house cookie(s) and/or associated contextual information by
employing the various aspects, for example.
The system 900 also includes one or more server(s) 904. The
server(s) 904 can also be hardware and/or software (e.g., threads,
processes, computing devices). The servers 904 can house threads to
perform transformations by employing the various aspects, for
example. One possible communication between a client 902 and a
server 904 can be in the form of a data packet adapted to be
transmitted between two or more computer processes. The data packet
may include a cookie and/or associated contextual information, for
example. The system 900 includes a communication framework 906
(e.g., a global communication network such as the Internet) that
can be employed to facilitate communications between the client(s)
902 and the server(s) 904.
Communications can be facilitated through a wired (including
optical fiber) and/or wireless technology (including non-radio
wireless communications). The client(s) 902 are operatively
connected to one or more client data store(s) 908 that can be
employed to store information local to the client(s) 902 (e.g.,
cookie(s) and/or associated contextual information). Similarly, the
server(s) 904 are operatively connected to one or more server data
store(s) 910 that can be employed to store information local to the
servers 904.
What has been described above includes examples of the various
aspects. It is, of course, not possible to describe every
conceivable combination of components or methodologies for purposes
of describing the various aspects, but one of ordinary skill in the
art may recognize that many further combinations and permutations
are possible. Accordingly, the subject specification intended to
embrace all such alterations, modifications, and variations.
In particular and in regard to the various functions performed by
the above described components, devices, circuits, systems and the
like, the terms (including a reference to a "means") used to
describe such components are intended to correspond, unless
otherwise indicated, to any component which performs the specified
function of the described component (e.g., a functional
equivalent), even though not structurally equivalent to the
disclosed structure, which performs the function in the herein
illustrated exemplary aspects. In this regard, it will also be
recognized that the various aspects include a system as well as a
computer-readable medium having computer-executable instructions
for performing the acts and/or events of the various methods.
In addition, while a particular feature may have been disclosed
with respect to only one of several implementations, such feature
may be combined with one or more other features of the other
implementations as may be desired and advantageous for any given or
particular application. To the extent that the terms "includes,"
and "including" and variants thereof are used in either the
detailed description or the claims, these terms are intended to be
inclusive in a manner similar to the term "comprising."
The term "or" as used in either the detailed description or the
claims is intended to mean an inclusive "or" rather than an
exclusive "or". That is, unless specified otherwise, or clear from
the context, the phrase "X employs A or B" is intended to mean any
of the natural inclusive permutations. That is, the phrase "X
employs A or B" is satisfied by any of the following instances: X
employs A; X employs B; or X employs both A and B. In addition, the
articles "a" and "an" as used in this application and the appended
claims should generally be construed to mea n "one or more" unless
specified otherwise or clear from the context to be directed to a
singular form.
Furthermore, the one or more aspects may be implemented as a
method, apparatus, or article of manufacture using standard
programming and/or engineering techniques to produce software,
firmware, hardware, or any combination thereof to control a
computer to implement the disclosed aspects. The term "article of
manufacture" (or alternatively, "computer program product") as used
herein is intended to encompass a computer program accessible from
any computer-readable device, carrier, or media. For example,
computer readable media can include but are not limited to magnetic
storage devices (e.g., hard disk, floppy disk, magnetic strips . .
. ), optical disks (e.g., compact disk (CD), digital versatile disk
(DVD) . . . ), smart cards, and flash memory devices (e.g., card,
stick). Additionally it should be appreciated that a carrier wave
can be employed to carry computer-readable electronic data such as
those used in transmitting and receiving electronic mail or in
accessing a network such as the Internet or a local area network
(LAN). Of course, those skilled in the art will recognize many
modifications may be made to this configuration without departing
from the scope of the disclosed aspects.
* * * * *
References