U.S. patent application number 13/684474 was filed with the patent office on 2013-04-25 for automatically finding contextually related items of a task.
This patent application is currently assigned to MICROSOFT CORPORATION. The applicant listed for this patent is Microsoft Corporation. Invention is credited to Kuldeep Karnawat, George Perantatos, John S. Wana.
Application Number | 20130103699 13/684474 |
Document ID | / |
Family ID | 43731513 |
Filed Date | 2013-04-25 |
United States Patent
Application |
20130103699 |
Kind Code |
A1 |
Perantatos; George ; et
al. |
April 25, 2013 |
AUTOMATICALLY FINDING CONTEXTUALLY RELATED ITEMS OF A TASK
Abstract
Architecture for enabling a user to automatically recover
documents and other information associated with work contexts and
recover documents and other information artifacts associated with a
specific project. The architecture enables monitoring and recording
of activity information related to user interactions with
information artifacts pertaining to a particular work context. The
user can select a document having a portion of work content (e.g.,
a term or other type of reference item in a document) related to
the work context. A lexical analysis is performed on the activity
information and the reference item to identify lexical
similarities. A list of candidate items (e.g., related documents)
is inferred from the information artifacts based on the lexical
similarities. The candidate items related to the work context are
presented to the user, who can select specific items to reestablish
the work context.
Inventors: |
Perantatos; George;
(Seattle, WA) ; Karnawat; Kuldeep; (Bellevue,
WA) ; Wana; John S.; (Seattle, WA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Microsoft Corporation; |
Redmond |
WA |
US |
|
|
Assignee: |
MICROSOFT CORPORATION
Redmond
WA
|
Family ID: |
43731513 |
Appl. No.: |
13/684474 |
Filed: |
November 23, 2012 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
|
12560435 |
Sep 16, 2009 |
8341175 |
|
|
13684474 |
|
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Current U.S.
Class: |
707/749 ;
707/736; 707/748 |
Current CPC
Class: |
G06Q 10/10 20130101;
G06F 16/93 20190101; G06F 16/24 20190101 |
Class at
Publication: |
707/749 ;
707/736; 707/748 |
International
Class: |
G06F 17/30 20060101
G06F017/30 |
Claims
1. A computer-implemented contextual system, comprising: an
activity component for monitoring and recording activity
information related to user interaction with information artifacts
in a work context; a reference artifact comprising at least one
reference item related to the work context; an analysis component
for performing lexical analysis on the activity information and the
reference item to identify lexical similarities; an inference
component that infers candidate items selected from the information
artifacts based on the lexical similarities; and a presentation
component for presenting the candidate items related to the work
context.
2. The system of claim 1, wherein the work context further
comprises at least one of document content, communications related
to the document content, location of the document content, or user
environment relating to the document content.
3. The system of claim 1, wherein the information artifacts
comprise at least one type of user recognizable and usable data
entity.
4. The system of claim 3, wherein the data entity further comprises
at least one of a file, a data stream, a web page, a spreadsheet,
an email, a calendar appointment, an instant messaging
conversation, a sticky note, or embedded metadata.
5. The system of claim 1, wherein the presentation component
further comprises a score assignment for hierarchically ranking the
candidate items.
6. The system of claim 1, further comprising a weighting component
for assigning a weighting factor to relevance of at least one
predetermined activity information item.
7. The system of claim 1, further comprising an actuable menu
element for manually displaying the candidate items in a user
interface.
8. A computer-implemented contextual method, comprising: monitoring
and recording activity information related to user interaction with
information artifacts in a work context; performing lexical
analysis on the activity information and a reference item related
to the work context to identify lexical similarities; inferring
candidate items selected from the information artifacts based on
the lexical similarities between the activity information and the
reference item resulting from the analysis; and presenting the
candidate items related to the work context.
9. The method of claim 8, further comprising reconstituting the
work context by processing the candidate items.
10. The method of claim 8, further comprising inferring respective
associated candidate items from a selected candidate item to obtain
a more precise set of candidate items.
11. The method of claim 8, further comprising hierarchically
ranking the candidate items in accordance with frequency of lexical
similarities between the activity information and the reference
item.
12. The method of claim 8, further comprising hierarchically
ranking the candidate items in accordance with frequency of
switches between the information artifacts and a reference document
containing the reference item.
13. The method of claim 8, further comprising automatically
monitoring the user interaction as a background process.
Description
CROSS-REFERENCE TO RELATED APPLICATIONS
[0001] This application is a Divisional of pending patent
application Ser. No. 12/560,435 entitled "AUTOMATICALLY FINDING
CONTEXTUALLY RELATED ITEMS OF A TASK" and filed Sep. 16, 2009.
BACKGROUND
[0002] Users switch contexts frequently when working. This can be
due to interruptions, and the need to put aside the work being
performed for a particular task in order to switch to another task.
Upon returning to the original task, users can have a problem
recalling all of the items upon which they were working (e.g.,
files, applications, locations, people, communications, etc.) in
order to resume the original task.
[0003] Problems related to context switching can be alleviated if
users rigorously kept records of everything and everyone involved
in a particular work context. However, it can be just as much work
creating a complete record of all the items used in a particular
work context than performing the actual work itself. Additionally,
certain items cannot be easily recorded. For example, it can be
difficult to save or reference an email message, an instant message
conversation, or an application that does not produce a file, such
as a calculator.
[0004] In practice, users typically rely on memory recall to
relocate and rebuild work contexts. However, this can be a
time-consuming and error-prone strategy. Users can also rely on
traditional search engines that accept keyword queries for locating
relevant web pages and other items. With a search engine, a
specific phrase or parameter is entered in order to locate relevant
items. While search engines produce results, the engines oftentimes
produce a great number of irrelevant results, and therefore, are
not helpful in recalling a specific set of items related to a
particular task.
[0005] Additionally, keyword search results merely present a list
of items containing relevant terms. Even if a target list of
relevant results is obtained from a keyword search, a search can
typically only retrieve documents, not application states. It can
be time-consuming to perform searches, with little assurance that a
precise list of previous work context items can be
reconstructed.
SUMMARY
[0006] The following presents a simplified summary in order to
provide a basic understanding of some novel embodiments described
herein. This summary is not an extensive overview, and it is not
intended to identify key/critical elements or to delineate the
scope thereof. Its sole purpose is to present some concepts in a
simplified form as a prelude to the more detailed description that
is presented later.
[0007] Architecture is disclosed for identifying items such as
documents and other types of information artifacts related to a
work context in which a user has worked, and enabling retrieval
(e.g., manual, automatic) of the items after leaving the original
context to reconstitute the work context effectively and
efficiently.
[0008] Multiple streams of information can be combined to
automatically infer contextual relationships for a given task. For
example, user activity such as switching between documents and
copy/paste operations can be monitored and recorded. A lexical
analysis is performed on the user activity and also with a
reference item to infer relationships between items worked on by
the user. Contextually-related items are presented to the user
based on the reference item, producing results that represent
related items for a specific user task worked on previously by the
user, rather than the broad, generalized results obtained from a
typical keyword-based search.
[0009] To the accomplishment of the foregoing and related ends,
certain illustrative aspects are described herein in connection
with the following description and the annexed drawings. These
aspects are indicative of the various ways in which the principles
disclosed herein can be practiced and all aspects and equivalents
thereof are intended to be within the scope of the claimed subject
matter. Other advantages and novel features will become apparent
from the following detailed description when considered in
conjunction with the drawings.
BRIEF DESCRIPTION OF THE DRAWINGS
[0010] FIG. 1 illustrates a computer-implemented contextual system
in accordance with the disclosed architecture.
[0011] FIG. 2 illustrates an alternative embodiment of a contextual
system that includes additional entities for background monitoring
and analysis.
[0012] FIG. 3 illustrates types of user interactions used with the
contextual system.
[0013] FIG. 4 illustrates an alternative embodiment of a contextual
system that includes additional entities for background collection,
querying, and storage.
[0014] FIG. 5 illustrates an alternative embodiment of a contextual
system.
[0015] FIG. 6 illustrates types of information artifacts used with
the contextual system.
[0016] FIG. 7 illustrates an alternative embodiment of a contextual
system that includes additional entities for scoring, weighting,
and manually displaying.
[0017] FIG. 8 illustrates an implementation for inferring
contextual relationships in accordance with the disclosed
architecture.
[0018] FIG. 9 illustrates a method of inferring contextual
relationships.
[0019] FIG. 10 illustrates additional aspects of the method of
inferring contextual relationships.
[0020] FIG. 11 illustrates a block diagram of a computing system
operable to provide inference of contextual relationships in
accordance with the disclosed architecture.
DETAILED DESCRIPTION
[0021] The disclosed architecture enables the automatic recovery of
documents and information artifacts associated a specific work
context and, the recovery of the documents and other information
artifacts associated when reconstituting the work context. The
architecture enables monitoring and recording of activity
information related to user interactions with information artifacts
pertaining to a particular work context. The user can select a
document having a term or other type of reference item related to
the work context. Analysis (e.g., lexical) can be performed on the
reference item and on documents having activity information related
to the reference item to identify similarities. A list of candidate
items (e.g., related documents) is inferred from the information
artifacts based on the similarities derived from the analysis. The
candidate items related to the work context are presented to the
user, who can then select, for example, specific items to
reconstitute the work context.
[0022] Reference is now made to the drawings, wherein like
reference numerals are used to refer to like elements throughout.
In the following description, for purposes of explanation, numerous
specific details are set forth in order to provide a thorough
understanding thereof. It may be evident, however, that the novel
embodiments can be practiced without these specific details. In
other instances, well known structures and devices are shown in
block diagram form in order to facilitate a description thereof.
The intention is to cover all modifications, equivalents, and
alternatives falling within the spirit and scope of the claimed
subject matter.
[0023] FIG. 1 illustrates a computer-implemented contextual system
100 in accordance with the disclosed architecture. An activity
component 102 is provided that monitors and records activity
information 104 related to user interaction with items 106
associated with a work context 108. The items 106 can be documents
or other types of information artifacts, as described in detail
hereinbelow. An analysis component 110 performs analysis (e.g.,
lexical) on a reference item 112 and the items 106 associated with
the activity information 104. An inference component 114 infers
candidate items 116 based on the activity information 104 and
results 118 of the analysis when reconstituting the work context
108.
[0024] As used herein, "context" can refer to content viewed, read
(e.g., reads between applications or an application and data),
and/or created by a user, where the content can be a text-based
document and/or other information artifact. Context can also refer
to communications associated with the content, such as emails or
instant messages. Additionally, context can refer to one or more
locations in which the content was read or used, which can include
websites, network or local folders, collaborative sites, etc.
Further, context can refer to an environment relating to the
content, such as a setting in which users were while dealing or
interacting with the content. The environment can be a personal
desktop in a meeting where certain participants were involved, for
example. Context can also refer to other individuals (e.g.,
collaborators and/or participants) associated with the
communications, locations, and environment, etc.
[0025] As illustrated in FIG. 1, the analysis component 110
performs lexical analysis on the reference item 112, which can be a
document or other type of information artifact previously opened by
the user. It is to be understood, however, that other types of
analysis can be utilized instead of or in combination with lexical
analysis. The activity information 104 is compared to determine
activity common to other items of the items 106. Common activity
can be a switch (navigation) between documents, for example. If
common activity to a subset of the items 106 is noted, lexical
analysis is performed on the subset to determine whether there are
lexical intersections between the items 106 and the reference item
112. These lexical intersections are output as the results 118. The
results 118 are compared with the activity information 104 (e.g.,
the number of switches between documents) to suggest the candidate
items 116.
[0026] As illustrated in FIG. 1, the contextual system 100 produces
results based on the reference item 112, in contrast with search
engines that rely on user entry of keywords to produce results that
match the keywords. For example, a user can return to a work
context of planning a site visit by starting with a currently
opened presentation document. The presentation document is the
reference item 112, and functions as the "query" rather than
keywords in a search. The contextual system 100 produces the
candidate items 116 by identifying intersections of user activity
with lexical analysis over that activity. In one aspect, the items
106 can be identified based on user activity and then
differentiated using lexical analysis, which can produce a very
precise list of the candidate items 116.
[0027] Instances of pure user activity with no lexical matches can
produce results that are not relevant, such as checking an
unrelated email or surfing an unrelated web page while working on a
document. Instances of pure lexical matching with no user activity
can yield unrelated items that happen to share the same words (e.g.
"site" in an email about a site visit as well as a website or a
drop site). Thus, instances that have both lexical and activity
matches represent high-precision results for the given work context
the user is trying to rebuild.
[0028] FIG. 2 illustrates an alternative embodiment of a contextual
system 200 that includes additional entities for background
monitoring and analysis. The activity component 102 can include a
background component 202 that monitors user activity as an
operation that is transparent to the user and without user
interaction. In this manner, the activity information 104 can be
obtained in the background without interrupting the user or
requiring any user feedback.
[0029] As illustrated in FIG. 2, the results 118 of the lexical
analysis can include processing common terms 204 found in the
reference item 112 and the candidate items 116. The lexical
analysis can examine content and metadata associated with the
information artifacts to cluster related content together based on
a starting point of content in the reference item 112. The common
terms 204 can include common senders and/or receivers in emails,
common noun phrases in subject lines, and indications of how
content is organized by the user, such as file folders, categories,
and tags, for example.
[0030] The common terms 204 can also specifically include common
nouns, noun-phrases, author names, and participants, found in
content or metadata. The nouns can be extracted from email subject
lines and document titles as lexical attributes, and can also be
extracted content, location/path, email sender/recipients, etc. Any
other suitable scheme can be employed where the user introduces a
level of additional information about the content.
[0031] FIG. 3 illustrates types of user interactions 300 that can
be used with the contextual system. The user interactions 300 can
include a switching operation 302 between the programs or data. The
switching operation 302 indicates co-accessed documents that can
indicate patterns and activities that suggest relationships between
documents. The switching operation 302 indicates the programs,
data, documents, and/or other information artifacts that were
accessed by the user proximate the time when the reference item 112
was in focus by the user. The switching operation 302 can be used
as a starting point for determining possible associations to the
initial document or email used as the reference item 112.
[0032] As illustrated in FIG. 3, the user interactions 300 can also
include a copy/paste operation 304 between the programs or data.
Other user interactions 300 can include an insertion operation 306
of the programs or data into an attachment, in an email or other
type of message, for example. A toggle frequency measurement 308
can be used to measure the number of instances of switching between
documents, which can be used to ascertain a common relationship for
work context 108. A timestamp operation 310 can be used to
determine whether the candidate items 116 were created, edited, or
saved within a suitable time period proximate the reference item
112. A bookmarking operation 312 can be used to determine whether a
resource was recorded for future reference. A link operation 314
can suggest common work context 108 since the link operation
provides access between documents. A save operation 316 of an item
as a copy in another location can be used to suggest the common
work context 108. Dwell time 318 (e.g., total activity time) spent
on an information artifact can be used to suggest the work context
108. For example, the dwell time 318 can be the total amount of
time spent working on a draft, or time spent reviewing the draft,
and can be measured as keyboard and/or mouse activity, for
example.
[0033] As illustrated in FIG. 3, an example is provided in which
the switching operation 302 can be used as the activity information
104 by the analysis component 110 and the inference component 114
to determine whether co-accessed documents share a common context.
Switching between documents can be recorded as indicating whether a
document was a source document or a target document in the
switching operation 302, whether the source document was closed
before switching to the target document, whether the target was in
focus (foreground) by the user for a predetermined minimum interval
(e.g., less than three seconds), whether the target was opened in
the background (e.g., opened in a web browser tab which was not in
focus), whether the source document was the first document switched
from after the target document was opened, and whether the target
document was the last document switched to before the source
document was closed. Additionally, the number of times the user
switches between particular documents can be recorded.
[0034] The aforementioned user interactions 300 (and combinations
thereof) can be evaluated by the analysis component 110 and the
inference component 114 for relevance in filtering document
co-access instances that do not indicate (with high probability)
documents with shared context. Other related metadata can be logged
subsequently for relevance in filtering results.
[0035] FIG. 4 illustrates an alternative embodiment of a contextual
system 400 that includes additional entities for background
collection, querying, and storage. A query component 402 can be
provided for explicitly querying for the candidate items 116
related to the reference item 112. The query component 402
explicitly examines the reference item 112 and queries the system
400 for related items to infer the candidate items 116. The
reference item 112 can be a selected document provided by the user
that includes a clue as a starting point.
[0036] Optionally, a collection component 404 can be provided for
collecting a set of the candidate items 116 and implicitly querying
the set for candidate items 116 related to the reference item 112.
In this manner, the collection component 404 implicitly infers the
reference item 112 and automatically collects items together to
present to the user, to identify related items without requiring
the user to provide a clue up front.
[0037] As illustrated in FIG. 4, a storage component 406 can be
provided for storing the activity information 104 locally and/or
remotely. The storage component 406 can be any suitable data
storage system used for local storage, such as a computer internal
or external hard drive, a writable CD or DVD, or any removable
memory component such as a flash drive. The storage component 406
can alternatively be any suitable data storage component for remote
storage, such as a network server or drive, a tape backup, or
offsite storage facility.
[0038] In one aspect, the contextual system (e.g., system 100, 200,
400) does not store user content but rather the activity
information 104 associated with the user. For example, if a
document is opened and multiple switching operations are performed
between the document and an email message, the switching operations
are logged onto a server as identification (ID) numbers that
reference the document and email message with a time stamp. No
document content is represented by the ID numbers. The ID numbers
can be resolved using a local desktop search index and
corresponding application programming interfaces (APIs) to perform
the lexical analysis. Thus, the contextual system can perform
lexical analysis for the document using local data stored in the
local desktop search index and activity information 104 can be
obtained from the server.
[0039] FIG. 5 illustrates an alternative embodiment of a contextual
system 500. The activity component 102 monitors and records the
activity information 104 related to user interaction with
information artifacts 502 in the work context 108. As described
hereinabove, the information artifacts 502 can be documents or
other types of data, as described in detail hereinbelow. A
reference artifact 504 includes one or more of the reference items
112 related to the work context. The analysis component 110
performs lexical analysis on the activity information 104 and the
reference item 112 to identify lexical similarities 506. The
inference component 114 infers the candidate items 116 selected
from the information artifacts 502 based on the lexical
similarities 506. A presentation component 508 is provided for
presenting the candidate items 116 related to the work context
108.
[0040] FIG. 6 illustrates types of information artifacts 502 that
can be used with a contextual system. The information artifacts 502
can include one or more types of user recognizable and usable data
entity 600. The data entity 600 can include a file 602 associated
with any type of application. The data entity 600 can also include
a data stream 604 such as any active process carrying information
that can be accessed. The data entity 600 can further include a web
page 606 that includes data objects consumable by a web browser or
other suitable reader.
[0041] The data entity 600 can additionally be a spreadsheet 608,
an email message 610, an IM conversation 612, a calendar
appointment 614, a sticky note 616, or embedded metadata 618
contained in any of the aforementioned types of the data entity
600, or any other type of the information artifacts 502. The
embedded metadata 618 can be included in a drawing document or
other non-text-based document, for example. It is to be appreciated
that the information artifacts 502 can include any types or formats
of documents or other data structure in which the user is involved
in creating or consuming.
[0042] The activity information 104 can be extended across
applications that do not produce the information artifacts 502. The
activity information 104 can be useful if it can preserve an
application state that can be used for reconstituting the work
context 108. For example, if a user is employing a spreadsheet and
switches between the spreadsheet and a calculator application, the
switching of the activity information 104 can be useful in
inferring the work context 108.
[0043] FIG. 7 illustrates an alternative embodiment of a contextual
system 700 that includes additional entities for scoring,
weighting, and manually displaying context information. The
presentation component 508 can include a score assignment 702 for
hierarchically ranking the candidate items 116. The score
assignment 702 enables the candidate items 116 to be ranked based
on additional information, such as a higher or lower value assigned
to a particular one of the user interactions 300 or the information
artifacts 502.
[0044] For example, the score assignment 702 can be a lexical score
based on the product of term frequency and inverse document
frequency (TFIDF):
TFIDF=.SIGMA.(Term Frequency*Inverse Document Frequency)
[0045] where, for each matching noun,
[0046] Term Frequency (normalized)=number of occurrences of the
matching noun in the document/total number of nouns in the
document, and
[0047] Inverse Document Frequency=In (T/L) (i.e., the natural log
of (Total number of documents/Number of documents containing the
noun)).
[0048] In another example, the score assignment 702 can be a
co-access score based on the product of switch frequency and
inverse document frequency (SFIDF):
SFIDF=Switch frequency*Inverse Document frequency,
[0049] where,
[0050] Switch Frequency (normalized)=number of switches with
reference document/total number of switches associated with the
document
[0051] Inverse Document Frequency=In (T/L) (i.e., the natural log
of (Total number of documents/Number of documents with at least one
switch with reference document)
[0052] Additionally, the lexical score and co-access score can be
combined into a single score. A greater number of reference points
can indicate a greater likelihood of a relevant match. Two
overlapping intersections can indicate greater relevance than a
single intersection and two overlapping intersections, such as a
document and an email both having lexical and activity
intersections with a web page, which increases the ranking of the
web page since there are two references rather than one to the work
context.
[0053] As illustrated in FIG. 7, a weighting component 704 can be
used to assign a weighting factor that indicates the relevance of
at least one predetermined activity information item. The weighting
component 704 can be used to assign more weighting or less
weighting to the relevance to a particular document or activity.
The weighting can be associated with the types of the user
interactions 300 and the information artifacts 502, in order to
assign greater or lesser value.
[0054] The copy/paste operation 304 can be assigned a high value in
inferring the work context 108, since content is being duplicated
between documents. The insertion of a document as an attachment to
an email, or inserting a link to the document, can be considered
highly related based on the nature of the activity, more so than
switching between documents. Additionally, weighting can be
considered based on the time between switches, where a quick series
of switches can suggest less relevance, for example.
[0055] As illustrated in FIG. 7, an actionable menu element 706 is
provided for manually displaying the candidate items 116 in a user
interface. The menu element 706 can be a button in a user interface
at a specific location in a document application. The button can be
clicked, and thereafter the user is presented with the set of the
candidate items 116 associated with the work context 108 of the
document. The candidate items 116 can include other documents,
related emails, web pages containing relevant terms opened with the
document, etc. The menu element 706 can also be a sidebar having an
automatically generated list of relevant items associated with a
particular task or project related to the document. Alternatively,
the menu element can be a desktop item, where a set of documents
are considered to be reference items, rather than the single
reference item 112.
[0056] FIG. 8 illustrates an implementation 800 for inferring
contextual relationships in accordance with the disclosed
architecture. The implementation 800 is an exemplary conceptual
diagram depicting five items being recorded by the contextual
system. A reference item 802 represents a portion of the work
context that the user is trying to resume (e.g., a "site visit
agenda" document). Four candidate items are available. Candidate
item1 804 is a document representing a "plan" and has some recorded
switch activity between this document and the reference item.
However, there is no lexical similarity between the candidate item1
804 and the reference item 802.
[0057] Candidate item2 806 relates to a "plan for site visit" and
has both recorded switching activity and lexical similarities
("site visit") with the reference item. Candidate item3 808
includes the terms "visit this web site" but has only lexical
similarities ("visit," "site") with the reference item. Candidate
item4 810 has both recorded copy/paste activity and lexical
similarities ("agenda") with the reference item. The result is that
only the candidate item2 806 and the candidate item4 810 are
returned as contextually related to the reference item 802.
[0058] An exemplary scenario follows herewith to demonstrate the
operation of the contextual architecture disclosed herein. User1, a
member of a marketing team, focuses on print and web advertisements
for a company. User 1 is working on a project when an IM is
received instructing User1 to find out which foreign country
magazines are suitable for advertizing specialty gear. User1 has
browsed some websites in the past pertaining to this subject, but
has not formally investigated the matter.
[0059] User1 creates a new space for this task (e.g., a workspace
or a folder), adds to the space the received IM and, locates and
adds to the space a presentation document that includes prior
research related to this task. The contextual system automatically
adds content to the space related to the creation or dissemination
of the content already in the space, such as the sources from which
User 1 copied and pasted to create the presentation document, and
emails sent that included relevant content. The contextual system
automatically suggests information artifacts related to the content
in the space by profiling information artifacts viewed and worked
on by User1, including activity related to the information
artifacts, to help reconstitute the work context. The suggestions
can be based on the content already in the space, matching
keywords, authors, and collaborators, and other information
artifacts open around the same time as the content in the
space.
[0060] Included herein is a set of flow charts representative of
exemplary methodologies for performing novel aspects of the
disclosed architecture. While, for purposes of simplicity of
explanation, the one or more methodologies shown herein, for
example, in the form of a flow chart or flow diagram, are shown and
described as a series of acts, it is to be understood and
appreciated that the methodologies are not limited by the order of
acts, as some acts may, in accordance therewith, occur in a
different order and/or concurrently with other acts from that shown
and described herein. For example, 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. Moreover, not all acts illustrated in a
methodology may be required for a novel implementation.
[0061] FIG. 9 illustrates a method of inferring contextual
relationships. At 900, monitoring and recording is performed for
activity information related to user interaction with information
artifacts in a work context. At 902, lexical analysis is performed
on the activity information and a reference item related to the
work context to identify lexical similarities. At 904, candidate
items are inferred where the candidate items are selected from the
information artifacts based on the lexical similarities between the
activity information and the reference item resulting from the
analysis. At 906, the candidate items related to the work context
are presented.
[0062] FIG. 10 illustrates additional aspects of the method of
inferring contextual relationships of FIG. 9. At 1000, the work
context is reconstituted by processing the candidate items. At
1002, respective associated candidate items are inferred from a
selected candidate item to obtain a more precise set of candidate
items. In this manner, a specific candidate item can become a
reference item for its own set of candidate items, and the latter
set can be compared to the initial set to produce an expanded set
of candidate items beyond only the candidate items inferred from
the original reference item.
[0063] As illustrated in FIG. 10, at 1004, the candidate items are
hierarchically ranked in accordance with frequency of lexical
similarities between the activity information and the reference
item. At 1006, the candidate items are hierarchically ranked in
accordance with frequency of switches between the information
artifacts and a reference document containing the reference item.
At 1008, the user interaction is automatically monitored as a
background process.
[0064] As disclosed herein, the contextual system and method infers
relationships between items worked on by a user by intersecting
activity information with lexical analysis of items related to the
activity information. The contextual system and method presents
contextually related items to a user based on a reference item, by
querying inferred relationships and thereby producing
high-precision results. The contextual system and method combines
two streams of information (i.e., user activity and lexical
analysis) to automatically infer contextual relationships
associated with a specific task or work context. The contextual
system and method provides monitoring of specific user activities
(including document switching, copy paste operations, insertions of
attachment or links, and/or bookmarking, as mentioned hereinabove)
from which to draw inferences. In this manner, the contextual
system and method produces high-precision results that represent
related items for a specific user task or work context.
[0065] Rather than simply providing results of a general
keyword-based search, the contextual system and method discovers
items related to a given work context or task based on interactions
with information artifacts seen and worked with previously by the
user. In this way, the contextual system and method can assist
users in returning to relevant content for a given task faster than
can be done otherwise. The contextual system and method can assist
users uncover content related to a particular task that might have
otherwise been forgotten. The contextual system and method can thus
enable the users to intuitively perceive at a glance whether
candidate items have a high value. The contextual system and method
is particularly useful in tasks associated with work contexts that
span multiple work sessions, more than a day or two apart, and also
multiple applications, and situations where the user has not filed
or recorded everything seen or done as part of the task.
[0066] As used in this application, the terms "component" and
"system" 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 can be, but is not
limited to being, a process running on a processor, a processor, a
hard disk drive, multiple storage drives (of optical, solid state,
and/or magnetic storage medium), 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 can reside within a process
and/or thread of execution, and a component can be localized on one
computer and/or distributed between two or more computers. The word
"exemplary" may be used herein 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.
[0067] Referring now to FIG. 11, there is illustrated a block
diagram of a computing system 1100 operable to execute inference of
contextual relationships in accordance with the disclosed
architecture. In order to provide additional context for various
aspects thereof, FIG. 11 and the following discussion are intended
to provide a brief, general description of the suitable computing
system 1100 in which the various aspects can be implemented. While
the description above is in the general context of
computer-executable instructions that can run on one or more
computers, those skilled in the art will recognize that a novel
embodiment also can be implemented in combination with other
program modules and/or as a combination of hardware and
software.
[0068] The computing system 1100 for implementing various aspects
includes the computer 1102 having processing unit(s) 1104, a system
memory 1106, and a system bus 1108. The processing unit(s) 1104 can
be any of various commercially available processors such as
single-processor, multi-processor, single-core units and multi-core
units. Moreover, those skilled in the art will appreciate that the
novel methods can be practiced with other computer system
configurations, including minicomputers, mainframe computers, as
well as personal computers (e.g., desktop, laptop, etc.), 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.
[0069] The system memory 1106 can include volatile (VOL) memory
1110 (e.g., random access memory (RAM)) and non-volatile memory
(NON-VOL) 1112 (e.g., ROM, EPROM, EEPROM, etc.). A basic
input/output system (BIOS) can be stored in the non-volatile memory
1112, and includes the basic routines that facilitate the
communication of data and signals between components within the
computer 1102, such as during startup. The volatile memory 1110 can
also include a high-speed RAM such as static RAM for caching
data.
[0070] The system bus 1108 provides an interface for system
components including, but not limited to, the memory subsystem 1106
to the processing unit(s) 1104. The system bus 1108 can be any of
several types of bus structure that can further interconnect to a
memory bus (with or without a memory controller), and a peripheral
bus (e.g., PCI, PCIe, AGP, LPC, etc.), using any of a variety of
commercially available bus architectures.
[0071] The computer 1102 further includes storage subsystem(s) 1114
and storage interface(s) 1116 for interfacing the storage
subsystem(s) 1114 to the system bus 1108 and other desired computer
components. The storage subsystem(s) 1114 can include one or more
of a hard disk drive (HDD), a magnetic floppy disk drive (FDD),
and/or optical disk storage drive (e.g., a CD-ROM drive DVD drive),
for example. The storage interface(s) 1116 can include interface
technologies such as EIDE, ATA, SATA, and IEEE 1394, for
example.
[0072] One or more programs and data can be stored in the memory
subsystem 1106, a removable memory subsystem 1118 (e.g., flash
drive form factor technology), and/or the storage subsystem(s) 1114
(e.g., optical, magnetic, solid state), including an operating
system 1120, one or more application programs 1122, other program
modules 1124, and program data 1126.
[0073] Generally, programs include routines, methods, data
structures, other software components, etc., that perform
particular tasks or implement particular abstract data types. All
or portions of the operating system 1120, applications 1122,
modules 1124, and/or data 1126 can also be cached in memory such as
the volatile memory 1110, for example. It is to be appreciated that
the disclosed architecture can be implemented with various
commercially available operating systems or combinations of
operating systems (e.g., as virtual machines).
[0074] The aforementioned application programs 1122, program
modules 1124, and program data 1126 can include the
computer-implemented system 100, the activity component 102, the
activity information 104, the items 106, the work context 108, the
analysis component 110, the reference item 112, the inference
component 114, the candidate items 116, and the results 118 of FIG.
1, the system 200 including further additional components such as
the background component 202 and the common terms 204 of FIG. 2,
the user interactions 300 including the switching operation 302,
the copy paste operation 304, the insertion operation 306, the
toggle frequency measurement 308, the timestamp operation 310, the
bookmarking operation 312, the link operation 314, the save
operation 316, and the dwell time 318 of FIG. 3, the system 400
including further additional components such as the query component
402, the collection component 404, and the storage component 406 of
FIG. 4.
[0075] The aforementioned application programs 1122, program
modules 1124, and program data 1126 can further include the system
500, which comprises additional components such as the information
artifacts 502, the reference artifact 504, the lexical similarities
506, and the presentation component 508 of FIG. 5, the user
recognizable and usable data entity 600, the file 602, the data
stream 604, the web page 606, the spreadsheet 608, the email
message 610, the instant messaging conversation 612, the calendar
appointment 614, the sticky note 616, and the embedded metadata 618
of FIG. 6, the system 700 including further additional components
such as the score assignment 702, the weighting component 704, and
the actionable menu element 706 of FIG. 7, and the implementation
800 including the reference item 802, the candidate item1 804, the
candidate item2 806, the candidate item3 808, and the candidate
item4 810 of FIG. 8, and the methods of FIGS. 9-10, for
example.
[0076] The storage subsystem(s) 1114 and memory subsystems (1106
and 1118) serve as computer readable media for volatile and
non-volatile storage of data, data structures, computer-executable
instructions, and so forth. Computer readable media can be any
available media that can be accessed by the computer 1102 and
includes volatile and non-volatile media, removable and
non-removable media. For the computer 1102, the media accommodate
the storage of data in any suitable digital format. It should be
appreciated by those skilled in the art that other types of
computer readable media can be employed such as zip drives,
magnetic tape, flash memory cards, cartridges, and the like, for
storing computer executable instructions for performing the novel
methods of the disclosed architecture.
[0077] A user can interact with the computer 1102, programs, and
data using external user input devices 1128 such as a keyboard and
a mouse. Other external user input devices 1128 can include a
microphone, an IR (infrared) remote control, a joystick, a game
pad, camera recognition systems, a stylus pen, touch screen,
gesture systems (e.g., eye movement, head movement, etc.), and/or
the like. The user can interact with the computer 1102, programs,
and data using onboard user input devices 1130 such a touchpad,
microphone, keyboard, etc., where the computer 1102 is a portable
computer, for example. These and other input devices are connected
to the processing unit(s) 1104 through input/output (I/O) device
interface(s) 1132 via the system bus 1108, but can be connected by
other interfaces such as a parallel port, IEEE 1394 serial port, a
game port, a USB port, an IR interface, etc. The I/O device
interface(s) 1132 also facilitate the use of output peripherals
1134 such as printers, audio devices, camera devices, and so on,
such as a sound card and/or onboard audio processing
capability.
[0078] One or more graphics interface(s) 1136 (also commonly
referred to as a graphics processing unit (GPU)) provide graphics
and video signals between the computer 1102 and external display(s)
1138 (e.g., LCD, plasma) and/or onboard displays 1140 (e.g., for
portable computer). The graphics interface(s) 1136 can also be
manufactured as part of the computer system board.
[0079] The computer 1102 can operate in a networked environment
(e.g., IP-based) using logical connections via a wired/wireless
communications subsystem 1142 to one or more networks and/or other
computers. The other computers can include workstations, servers,
routers, personal computers, microprocessor-based entertainment
appliances, peer devices or other common network nodes, and
typically include many or all of the elements described relative to
the computer 1102. The logical connections can include
wired/wireless connectivity to a local area network (LAN), a wide
area network (WAN), hotspot, and so on. 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 such as
the Internet.
[0080] When used in a networking environment the computer 1102
connects to the network via a wired/wireless communication
subsystem 1142 (e.g., a network interface adapter, onboard
transceiver subsystem, etc.) to communicate with wired/wireless
networks, wired/wireless printers, wired/wireless input devices
1144, and so on. The computer 1102 can include a modem or other
means for establishing communications over the network. In a
networked environment, programs and data relative to the computer
1102 can be stored in the remote memory/storage device, as is
associated with a distributed system. 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.
[0081] The computer 1102 is operable to communicate with
wired/wireless devices or entities using the radio technologies
such as the IEEE 802.xx family of standards, such as wireless
devices operatively disposed in wireless communication (e.g., IEEE
802.11 over-the-air modulation techniques) with, for example, a
printer, scanner, desktop and/or portable computer, personal
digital assistant (PDA), communications satellite, any piece of
equipment or location associated with a wirelessly detectable tag
(e.g., a kiosk, news stand, restroom), and telephone. This includes
at least Wi-Fi (or Wireless Fidelity) for hotspots, WiMax, and
Bluetooth.TM. wireless technologies. Thus, the communications can
be a predefined structure as with a conventional network or simply
an ad hoc communication between at least two devices. Wi-Fi
networks use radio technologies called IEEE 802.11x (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 wire networks (which use IEEE 802.3-related media
and functions).
[0082] The illustrated aspects can 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 local and/or remote storage and/or memory
system.
[0083] What has been described above includes examples of the
disclosed architecture. It is, of course, not possible to describe
every conceivable combination of components and/or methodologies,
but one of ordinary skill in the art may recognize that many
further combinations and permutations are possible. Accordingly,
the novel architecture is intended to embrace all such alterations,
modifications and variations that fall within the spirit and scope
of the appended claims. Furthermore, to the extent that the term
"includes" is used in either the detailed description or the
claims, such term is intended to be inclusive in a manner similar
to the term "comprising" as "comprising" is interpreted when
employed as a transitional word in a claim.
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