U.S. patent application number 12/896649 was filed with the patent office on 2011-09-22 for system and method for providing predictive contacts.
This patent application is currently assigned to Avaya Inc.. Invention is credited to Krishna Kishore DHARA, Venkatesh KRISHNASWAMY, Eunsoo SHIM, Xiaotao WU.
Application Number | 20110231396 12/896649 |
Document ID | / |
Family ID | 44647259 |
Filed Date | 2011-09-22 |
United States Patent
Application |
20110231396 |
Kind Code |
A1 |
DHARA; Krishna Kishore ; et
al. |
September 22, 2011 |
SYSTEM AND METHOD FOR PROVIDING PREDICTIVE CONTACTS
Abstract
Disclosed herein are systems, methods, and non-transitory
computer-readable storage media for providing predictive contacts.
A system configured to practice the method first analyzes a
communication history and a current usage context of a user. Based
on the analysis, the system ranks contacts that the user is likely
to communicate with from a list of contacts to yield ranked
contacts. The system identifies a respective motive for ranking
each contact, and presents a predictive list of contacts based at
least in part on the ranked contacts, wherein each ranked contact
in the predictive list of contacts includes at least part of the
respective motive. The system can update the predictive list of
contacts in real time as the current usage context changes. The
communication history can include, for example, emails, instant
messages, phone calls, video conferences, and calendar events. The
motive can include a user interaction history with a particular
contact.
Inventors: |
DHARA; Krishna Kishore;
(DAYTON, NJ) ; KRISHNASWAMY; Venkatesh; (HOLMDEL,
NJ) ; SHIM; Eunsoo; (PRINCETON JUNCTION, NJ) ;
WU; Xiaotao; (EDISON, NJ) |
Assignee: |
Avaya Inc.
Basking Ridge
NJ
|
Family ID: |
44647259 |
Appl. No.: |
12/896649 |
Filed: |
October 1, 2010 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
|
61315719 |
Mar 19, 2010 |
|
|
|
Current U.S.
Class: |
707/731 ;
707/751; 707/E17.009; 707/E17.016 |
Current CPC
Class: |
H04L 47/70 20130101;
G06Q 10/1095 20130101; H04M 3/565 20130101; H04L 65/403 20130101;
H04L 12/1818 20130101 |
Class at
Publication: |
707/731 ;
707/751; 707/E17.009; 707/E17.016 |
International
Class: |
G06F 17/30 20060101
G06F017/30 |
Claims
1. A method of providing predictive contacts, the method
comprising: analyzing a communication history and a current usage
context of a user to yield an analysis; based on the analysis,
ranking contacts that the user is likely to communicate with from a
list of contacts to yield ranked contacts; identifying a respective
motive for ranking each contact; and presenting a predictive list
of contacts based at least in part on the ranked contacts, wherein
each ranked contact in the predictive list of contacts includes at
least part of the respective motive.
2. The method of claim 1, wherein the predictive list of contacts
is updated in real time as the current usage context changes.
3. The method of claim 1, wherein the communication history
includes at least one of an email history, an instant messaging
history, a call history, a video conference history, and a past
calendar event.
4. The method of claim 1, wherein the respective motive comprises a
history of interactions between the user and a respective
contact.
5. The method of claim 1, further comprising: identifying a likely
communication modality for each ranked contact; and presenting, as
part of the predictive list of contacts, the likely communication
modality.
6. The method of claim 1, further comprising: receiving from the
user a request to ignore one respective motive; reranking the
contacts based on the request to ignore the one respective motive
to yield reranked contacts; and presenting an updated predictive
list of contacts based at least in part on the reranked
contacts.
7. The method of claim 1, wherein the communication history is
stored on a plurality of communications devices.
8. A system for providing predictive contacts, the method
comprising: a processor; a first module configured to control the
processor to analyze a communication history and a current usage
context of a user to yield an analysis; a second module configured
to control the processor, based on the analysis, to rank contacts
that the user is likely to communicate with from a list of contacts
to yield ranked contacts; a fourth module configured to control the
processor to identify a respective motive for ranking each contact;
and a fifth module configured to control the processor to present a
predictive list of contacts based at least in part on the ranked
contacts, wherein each ranked contact in the predictive list of
contacts includes at least part of the respective motive.
9. The system of claim 8, wherein the predictive list of contacts
is updated in real time as the current usage context changes.
10. The system of claim 8, wherein the communication history
includes at least one of an email history, an instant messaging
history, a call history, a video conference history, and a past
calendar event.
11. The system of claim 8, wherein the respective motive comprises
a history of interactions between the user and a respective
contact.
12. The system of claim 8, further comprising: a sixth module
configured to control the processor to identify a likely
communication modality for each ranked contact; and a seventh
module configured to control the processor to present, as part of
the predictive list of contacts, the likely communication
modality.
13. The system of claim 8, further comprising: a sixth module
configured to control the processor to receive from the user a
request to ignore one respective motive; a seventh module
configured to control the processor to rerank the contacts based on
the request to ignore the one respective motive to yield reranked
contacts; and an eighth module configured to control the processor
to present an updated predictive list of contacts based at least in
part on the reranked contacts.
14. The system of claim 8, wherein the communication history is
stored on a plurality of communications devices.
15. A non-transitory computer-readable storage medium storing
instructions which, when executed by a computing device, cause the
computing device to provide predictive contacts, the instructions
comprising: analyzing a communication history and a current usage
context of a user to yield an analysis; based on the analysis,
ranking contacts that the user is likely to communicate with from a
list of contacts to yield ranked contacts; identifying a respective
motive for ranking each contact; and presenting a predictive list
of contacts based at least in part on the ranked contacts, wherein
each ranked contact in the predictive list of contacts includes at
least part of the respective motive.
16. The non-transitory computer-readable storage medium of claim
15, wherein the predictive list of contacts is updated in real time
as the current usage context changes.
17. The non-transitory computer-readable storage medium of claim
15, wherein the communication history includes at least one of an
email history, an instant messaging history, a call history, a
video conference history, and a past calendar event.
18. The non-transitory computer-readable storage medium of claim
15, wherein the respective motive comprises a history of
interactions between the user and a respective contact.
19. The non-transitory computer-readable storage medium of claim
15, the instructions further comprising: identifying a likely
communication modality for each ranked contact; and presenting, as
part of the predictive list of contacts, the likely communication
modality.
20. The non-transitory computer-readable storage medium of claim
15, the instructions further comprising: receiving from the user a
request to ignore one respective motive; reranking the contacts
based on the request to ignore the one respective motive to yield
reranked contacts; and presenting an updated predictive list of
contacts based at least in part on the reranked contacts.
Description
RELATED APPLICATIONS
[0001] This application claims priority to U.S. Provisional
Application 61/315,719, filed 19 Mar. 2010, the contents of which
are herein incorporated by reference in their entirety.
BACKGROUND
[0002] 1. Technical Field
[0003] The present disclosure relates to managing contacts and more
specifically to providing a context-sensitive list of predictive
contacts.
[0004] 2. Introduction
[0005] Traditional approaches to contacts require users to manually
manage and maintain their contacts. This approach is workable when
the list of contacts tends to be more static, but when the list of
contacts is more dynamic, manual contact management quickly becomes
difficult and excessively time consuming to maintain. When a list
of contacts grows too large, users often simply revert to a search
of LDAP, Post, Active Directory, or other similar directories to
obtain contact information.
[0006] Further, traditional approaches to listing contacts are
static. These static approaches to listing contacts can waste user
time. For example, a user must search for a contact even if the
contact was recently used. Some existing approaches attempt to sort
contacts in a predictive order, but these approaches are based on a
limited set of information, do not take in to account a current
user context, and do not indicate why or how the list of contacts
are ordered in a particular way.
[0007] One significant shortfall of the approaches known in the art
is that they do not provide a mechanism whereby, at any given time,
given a set of communication sessions (email, voice, chat, etc) and
their interactions (missed, answered), present solutions do not
provide a way of predicting which contacts the user needs. Also,
the present solutions do not provide transparency showing how or
why a set of contacts is ordered in a particular way and do not
account for a sufficiently broad interaction/communication data
set.
SUMMARY
[0008] Additional features and advantages of the disclosure will be
set forth in the description which follows, and in part will be
obvious from the description, or can be learned by practice of the
herein disclosed principles. The features and advantages of the
disclosure can be realized and obtained by means of the instruments
and combinations particularly pointed out in the appended claims.
These and other features of the disclosure will become more fully
apparent from the following description and appended claims, or can
be learned by the practice of the principles set forth herein.
[0009] Disclosed are systems, methods, and non-transitory
computer-readable storage media for providing predictive contacts.
A system configured to practice the method first analyzes a
communication history and a current usage context of a user. The
communication history can include emails, instant messaging, phone
calls, video conferences, and calendar events. Based on the
analysis, the system ranks contacts that the user is likely to
communicate with from a list of contacts to yield ranked contacts
and identifies a respective motive for ranking each contact. The
motive can include a history of interactions between the user and a
respective contact. Then the system presents a predictive list of
contacts based at least in part on the ranked contacts, wherein
each ranked contact in the predictive list of contacts includes at
least part of the respective motive. In one regard, the system
updates the predictive list of contacts in real time as the current
usage context changes.
[0010] In one variation, the system further identifies a likely
communication modality for each ranked contact, and presents, as
part of the predictive list of contacts, the likely communication
modality. In another variation, the system receives from the user a
request to ignore one respective motive. In response, the system
reranks the contacts based on the request to ignore the one
respective motive, and presents an updated predictive list of
contacts based at least in part on the reranked contacts.
BRIEF DESCRIPTION OF THE DRAWINGS
[0011] In order to describe the manner in which the above-recited
and other advantages and features of the disclosure can be
obtained, a more particular description of the principles briefly
described above will be rendered by reference to specific
embodiments thereof which are illustrated in the appended drawings.
Understanding that these drawings depict only exemplary embodiments
of the disclosure and are not therefore to be considered to be
limiting of its scope, the principles herein are described and
explained with additional specificity and detail through the use of
the accompanying drawings in which:
[0012] FIG. 1 illustrates an example system embodiment;
[0013] FIG. 2 illustrates an exemplary communications
environment;
[0014] FIG. 3 illustrates an exemplary list of predictive contacts;
and
[0015] FIG. 4 illustrates an example method embodiment.
DETAILED DESCRIPTION
[0016] Various embodiments of the disclosure are discussed in
detail below. While specific implementations are discussed, it
should be understood that this is done for illustration purposes
only. A person skilled in the relevant art will recognize that
other components and configurations may be used without parting
from the spirit and scope of the disclosure.
[0017] The present disclosure addresses the need in the art for
providing predictive contacts based on context. A system, method
and non-transitory computer-readable media are disclosed which
provide predictive contacts. A brief introductory description of a
basic general purpose system or computing device in FIG. 1 which
can be employed to practice the concepts is disclosed herein.
Afterward, the disclosure turns to a more detailed description of
the methods and environments in which the system can provide
predictive contacts. The disclosure now turns to FIG. 1.
[0018] With reference to FIG. 1, an exemplary system 100 includes a
general-purpose computing device 100, including a processing unit
(CPU or processor) 120 and a system bus 110 that couples various
system components including the system memory 130 such as read only
memory (ROM) 140 and random access memory (RAM) 150 to the
processor 120. The system 100 can include a cache of high speed
memory connected directly with, in close proximity to, or
integrated as part of the processor 120. The system 100 copies data
from the memory 130 and/or the storage device 160 to the cache for
quick access by the processor 120. In this way, the cache provides
a performance boost that avoids processor 120 delays while waiting
for data. These and other modules can control or be configured to
control the processor 120 to perform various actions. Other system
memory 130 may be available for use as well. The memory 130 can
include multiple different types of memory with different
performance characteristics. It can be appreciated that the
disclosure may operate on a computing device 100 with more than one
processor 120 or on a group or cluster of computing devices
networked together to provide greater processing capability. The
processor 120 can include any general purpose processor and a
hardware module or software module, such as module 1 162, module 2
164, and module 3 166 stored in storage device 160, configured to
control the processor 120 as well as a special-purpose processor
where software instructions are incorporated into the actual
processor design. The processor 120 may essentially be a completely
self-contained computing system, containing multiple cores or
processors, a bus, memory controller, cache, etc. A multi-core
processor may be symmetric or asymmetric.
[0019] The system bus 110 may be any of several types of bus
structures including a memory bus or memory controller, a
peripheral bus, and a local bus using any of a variety of bus
architectures. A basic input/output (BIOS) stored in ROM 140 or the
like, may provide the basic routine that helps to transfer
information between elements within the computing device 100, such
as during start-up. The computing device 100 further includes
storage devices 160 such as a hard disk drive, a magnetic disk
drive, an optical disk drive, tape drive or the like. The storage
device 160 can include software modules 162, 164, 166 for
controlling the processor 120. Other hardware or software modules
are contemplated. The storage device 160 is connected to the system
bus 110 by a drive interface. The drives and the associated
computer readable storage media provide nonvolatile storage of
computer readable instructions, data structures, program modules
and other data for the computing device 100. In one aspect, a
hardware module that performs a particular function includes the
software component stored in a non-transitory computer-readable
medium in connection with the necessary hardware components, such
as the processor 120, bus 110, display 170, and so forth, to carry
out the function. The basic components are known to those of skill
in the art and appropriate variations are contemplated depending on
the type of device, such as whether the device 100 is a small,
handheld computing device, a desktop computer, or a computer
server.
[0020] Although the exemplary embodiment described herein employs
the hard disk 160, it should be appreciated by those skilled in the
art that other types of computer readable media which can store
data that are accessible by a computer, such as magnetic cassettes,
flash memory cards, digital versatile disks, cartridges, random
access memories (RAMs) 150, read only memory (ROM) 140, a cable or
wireless signal containing a bit stream and the like, may also be
used in the exemplary operating environment. Non-transitory
computer-readable storage media expressly exclude media such as
energy, carrier signals, electromagnetic waves, and signals per
se.
[0021] To enable user interaction with the computing device 100, an
input device 190 represents any number of input mechanisms, such as
a microphone for speech, a touch-sensitive screen for gesture or
graphical input, keyboard, mouse, motion input, speech and so
forth. An output device 170 can also be one or more of a number of
output mechanisms known to those of skill in the art. In some
instances, multimodal systems enable a user to provide multiple
types of input to communicate with the computing device 100. The
communications interface 180 generally governs and manages the user
input and system output. There is no restriction on operating on
any particular hardware arrangement and therefore the basic
features here may easily be substituted for improved hardware or
firmware arrangements as they are developed.
[0022] For clarity of explanation, the illustrative system
embodiment is presented as including individual functional blocks
including functional blocks labeled as a "processor" or processor
120. The functions these blocks represent may be provided through
the use of either shared or dedicated hardware, including, but not
limited to, hardware capable of executing software and hardware,
such as a processor 120, that is purpose-built to operate as an
equivalent to software executing on a general purpose processor.
For example the functions of one or more processors presented in
FIG. 1 may be provided by a single shared processor or multiple
processors. (Use of the term "processor" should not be construed to
refer exclusively to hardware capable of executing software.)
Illustrative embodiments may include microprocessor and/or digital
signal processor (DSP) hardware, read-only memory (ROM) 140 for
storing software performing the operations discussed below, and
random access memory (RAM) 150 for storing results. Very large
scale integration (VLSI) hardware embodiments, as well as custom
VLSI circuitry in combination with a general purpose DSP circuit,
may also be provided.
[0023] The logical operations of the various embodiments are
implemented as: (1) a sequence of computer implemented steps,
operations, or procedures running on a programmable circuit within
a general use computer, (2) a sequence of computer implemented
steps, operations, or procedures running on a specific-use
programmable circuit; and/or (3) interconnected machine modules or
program engines within the programmable circuits. The system 100
shown in FIG. 1 can practice all or part of the recited methods,
can be a part of the recited systems, and/or can operate according
to instructions in the recited non-transitory computer-readable
storage media. Such logical operations can be implemented as
modules configured to control the processor 120 to perform
particular functions according to the programming of the module.
For example, FIG. 1 illustrates three modules Mod1 162, Mod2 164
and Mod3 166 which are modules configured to control the processor
120. These modules may be stored on the storage device 160 and
loaded into RAM 150 or memory 130 at runtime or may be stored as
would be known in the art in other computer-readable memory
locations.
[0024] Having discussed some basic computing system components, the
disclosure returns to a discussion of generating and presenting
predictive contacts. This approach generates a predictive list of
relevant contacts based on multiple sources of information, such as
email history, IM history, call frequency, and so forth. A user
services layer can mine information from many sources that a
predictive contact widget can use to generate a list of contacts.
In one aspect, the predictive contact component is a separate
hardware and/or software widget that operates in conjunction with
traditional communications equipment, but the predictive contact
elements described herein can also be incorporated with
communications equipment. The system uses that information and can
add `user context` at any given time to predict the contacts needed
by the user. The system can use an algorithm to rank all or part of
the contacts based on the above information. The system can display
on a user interface a widget or other notification presenting a
short history of a user's interactions with each predicted contact
(i.e. a brief summary of recent communications with them). This
solution presents a list of predicted people that the user may want
to contact. The system can dynamically update the list based on a
current communication, such as an email being received, or based on
the user's activities or context.
[0025] This approach provides users with a dynamic contact list
without manual contact management. This approach uses information
gleaned from many different communications modalities (email, IM,
voice calls, conference calls, text messages, etc.) to determine a
predictive contact list. Further, a user of a predictive contact
list can drill down to determine which communications or what
pieces of information in a communications history led to a
particular contact's placement in the predictive contact list.
[0026] This approach provides at least three benefits. First, this
approach provides an optimal predictive list of people likely to be
contacted by the user. Second, this approach provides the user
access to the "why" behind the list of contacts and the order of
the contacts. Third, this approach generates the list based on a
variety of different communication paths (email, calls, IM, and so
forth) and users' context at any given time.
[0027] FIG. 2 illustrates an exemplary communications environment
200 in which the predictive contacts system can operate. In this
environment 200, a user 202 uses a computing device 204, such as a
smart phone, desktop computer, laptop computer, tablet computing
device, or smart television set-top box, to communicate via a
communication network 206 with another user 208. Either as part of
a periodic, continuous, or event-driven process, the device 204
and/or other system monitors the user's current communication
context and compares that context with a communication history 212.
Based on similarities in context, content, or other factors, the
device 204 and/or other system selects and presents a list of
predictive contacts from a list of contacts 210. In one aspect, the
list of contacts 212 can be stored entirely or partially in a
network-based database 214, as can the communication history
216.
[0028] For example, if the user 202 is talking to Bob via instant
messaging and discussing Fred, the system can analyze the content
of the instant messaging session (for example, the discussion of
Fred) and the context of the instant messaging session (for
example, a discussion with Bob at a particular time of day and day
of the week). Based on the analysis, the system predicts that the
user 202 and Bob are likely to want to talk with Fred. The system
can assign Fred a higher ranking in the list of predictive contacts
in real time to reflect that content and context. In this way, the
user 202 can click on the list item for Fred and view current
information for Fred based on Fred's presence and can also initiate
a separate communication session with Fred or can request that Fred
join the current communication session between the user 202 and
Bob.
[0029] FIG. 3 illustrates an exemplary list of predictive contacts
300, such as the device 204 would display as part of a graphical
user interface to the user 202. This exemplary list of predictive
contacts 300 includes three entries: Mary 302, Joe 304, and Nick
306. The list of predictive contacts can be pure text or can
include multimedia content. For example, each predictive contact in
the list includes a profile image, a name, a telephone number, and
an email. Each contact can include more or less information. For
example, the contact listing can include presence information,
available communication modalities, social network information,
notes, memos, and other information describing the contact or the
contact's relationship to the user. In addition, each contact can
include at least part of the motivation 308, 310, 312 for their
appearance and placement in the predictive list of contacts. In one
motivation 308, the list includes an excerpt of a communication log
between the user and Mary from which the system deduced that the
user is very likely to contact Mary at this time. In another
motivation 310, the list includes an explanation of the high
ranking of Joe based on a pattern of calling Joe on Fridays. In yet
another motivation 312, the list bases the ranking on a somewhat
frequent and loosely connected pattern of communication sessions.
For example, the system tracks that after the user speaks with his
manager, the user sometimes calls Nick. Thus, after the user speaks
with his manager, the system can bump the ranking of Nick so Nick
moves up the list. As time passes after the user's conversation
with the manager, the system can apply a decay rate to Nick's
ranking so he is gradually ranked lower. For example, if, when the
user calls Nick after speaking to his manager, the user typically
calls within three minutes of speaking with his manager, the system
can rank Nick in a high position for three minutes and then rapidly
decrease Nick's ranking thereafter.
[0030] Having disclosed some basic system components and concepts,
the disclosure now turns to the exemplary method embodiment for
providing predictive contacts as shown in FIG. 4. For the sake of
clarity, the method is discussed in terms of an exemplary system
100 as shown in FIG. 1 configured to practice the method. The
system 100 first analyzes a communication history and a current
usage context of a user to yield an analysis (402). The
communication history can include, for example, an email history,
an instant messaging history, a call history, a video conference
history, and a past calendar event. Different portions of the
communication history can be stored on different communications
devices, such as users' cell phones, a network-based communication
server, a web calendar, and so forth. In one aspect, the system
operates in real time and constantly compares the current usage
context to the communication history. In another aspect, the system
is triggered by a discrete event, such as receiving an incoming
instant message or the user picking up a telephone receiver to make
a call. The current usage context can include data such as time of
day, day of the week, previous communications (including the
timing, content, participants, and metadata of previous
communications), location, a user identity, a calendar of past,
current, and/or future events, and so forth.
[0031] Based on the analysis, the system 100 ranks contacts that
the user is likely to communicate with from a list of contacts to
yield ranked contacts (404). In one aspect, the system assigns each
contact a probability score. In another aspect, the system only
processes a subset of the entire list of contacts. In a blended
approach, the system performs a very speed efficient, perhaps not
as accurate, initial ranking, and then uses the initial ranking to
cull less likely candidates from a more comprehensive, and perhaps
more time intensive, ranking process. The system can also sort or
filter ranked contacts using different sets of criteria, such as
phone calls, instant messages, contacts with whom the user has
communicated in the last 30 days, and so forth.
[0032] The system 100 identifies a respective motive for ranking
each contact (406). The system can identify the motive for ranking
each contact as the system ranks the contacts. The motive can
include, for example, a history of interactions between the user
and a respective contact. In this case, the system can highly rank
a first contact in touch with the user on a daily basis. The
motivation for the first contact's high rank is the consistent
communication history. The system can highly rank a second contact
because the user left a message on the answering machine of the
second contact and indicated in the message that the user would
call again soon. In this case, the motivation for the second
contact's high rank is that the user said he would call again soon
despite not having the lengthy communication history like the first
contact. The motivation can include multiple factors. The user can
filter and sort predictive contacts based on one or more factor.
For example, the system can receive from the user a request to
ignore one respective motive, rerank the contacts based on the
request to ignore the one respective motive, and present an updated
predictive list of contacts based at least in part on the reranked
contacts.
[0033] Further, the system 100 can base the motive at least
partially on how sent communications are viewed with respect to
received communications. This portion of the motive reflects how a
user's interest in incoming messages based on the user's responses
and corresponding actions with respect to the incoming messages.
For example, if Amy receives hourly emails from Fred, the system
100 might be inclined to assign Fred a higher ranking However, if
Amy has no interest in Fred or what Fred has to say, she may delete
Fred's emails without reading them or after having the email open
for only a few moments. Such interactions are not indicative of a
strong connection between Amy and Fred. The system 100 can analyze
such interactions and assign Fred a lower ranking Alternatively, if
Amy opens each of Fred's emails and replies in an average of 2
minutes or less, the system 100 can assign Fred a higher ranking.
The system 100 can consider several factors for sent messages. One
exemplary factor is the mean arrival time of received messages
versus mean sent time for sent messages. This factor can predict
whom the user is likely to contact at any given time and/or after
receiving a message from a person. Another exemplary factor is the
time difference between received messages and sent messages. A low
value can indicate to the system 100 that a contact is of high
importance to the user. A high value can indicate to the system 100
that the user either never responds to certain messages or takes a
long time to respond. Thus, if the user never responds to a message
from a certain contact, then the mean sent time is very high and
the system does not rank that contact very highly regardless of how
many times that contact sends (or attempts to send) the user
messages. These and similar factors can influence how the system
100 ranks contacts.
[0034] In another aspect, the system 100 is tied into social media,
such as Facebook or Twitter. If the user is friends with a contact
on Facebook, the system can boost the ranking for that contact. The
system can also adjust the ranking based on a number of other
social network elements, such as a number of shared contacts, how
often the user has viewed that contact's profile page or feed,
total time the user has spent interacting with the contact via
social media, and so forth. On the other hand, if a user has
removed a contact as a friend on Facebook or has recently denied a
friend request from the contact, the system 100 can rank that
contact lower.
[0035] The system 100 can determine the motive and/or rankings
based on other extra-system factors. For example, during working
hours, a user's immediate supervisor is ranked highly and the
motivation can be based on the fact that the time of day indicates
that the user is at work. After working hours, the system 100 can
rank the supervisor much lower based on the motivation that the
user is not `on the clock`. Similarly, user location can influence
motive. For example, if the user is in New York City, the system
100 can boost the ranking of contacts residing or working in and
around New York City. The system 100 can apply an algorithm to
modify rankings, such as an inverse square algorithm where a small
distance between a contact and the user corresponds to a large
increase to that contact's ranking and a large distance corresponds
accordingly to a small increase in the ranking, no change in the
ranking, or a reduction of the ranking.
[0036] The system 100 can assemble a composite of multiple factors
and/or motivations, including the ones described above and others,
to arrive at a particular ranking value for a contact.
[0037] The system 100 presents a predictive list of contacts based
at least in part on the ranked contacts, wherein each ranked
contact in the predictive list of contacts includes at least part
of the respective motive (408). The system can present the
predictive list of contacts with real time updates as the current
usage context changes. In another aspect, the system presents the
predictive list of contacts in response to a discrete event that
triggers the system to generate a predictive list of contacts. In
one modification, the system identifies a likely communication
modality for each ranked contact and presents, as part of the
predictive list of contacts, the likely communication modality.
[0038] Embodiments within the scope of the present disclosure may
also include tangible and/or non-transitory computer-readable
storage media for carrying or having computer-executable
instructions or data structures stored thereon. Such non-transitory
computer-readable storage media can be any available media that can
be accessed by a general purpose or special purpose computer,
including the functional design of any special purpose processor as
discussed above. By way of example, and not limitation, such
non-transitory computer-readable media can include RAM, ROM,
EEPROM, CD-ROM or other optical disk storage, magnetic disk storage
or other magnetic storage devices, or any other medium which can be
used to carry or store desired program code means in the form of
computer-executable instructions, data structures, or processor
chip design. When information is transferred or provided over a
network or another communications connection (either hardwired,
wireless, or combination thereof) to a computer, the computer
properly views the connection as a computer-readable medium. Thus,
any such connection is properly termed a computer-readable medium.
Combinations of the above should also be included within the scope
of the computer-readable media.
[0039] Computer-executable instructions include, for example,
instructions and data which cause a general purpose computer,
special purpose computer, or special purpose processing device to
perform a certain function or group of functions.
Computer-executable instructions also include program modules that
are executed by computers in stand-alone or network environments.
Generally, program modules include routines, programs, components,
data structures, objects, and the functions inherent in the design
of special-purpose processors, etc. that perform particular tasks
or implement particular abstract data types. Computer-executable
instructions, associated data structures, and program modules
represent examples of the program code means for executing steps of
the methods disclosed herein. The particular sequence of such
executable instructions or associated data structures represents
examples of corresponding acts for implementing the functions
described in such steps.
[0040] Those of skill in the art will appreciate that other
embodiments of the disclosure may be practiced in network computing
environments with many types of computer system configurations,
including personal computers, hand-held devices, multi-processor
systems, microprocessor-based or programmable consumer electronics,
network PCs, minicomputers, mainframe computers, and the like.
Embodiments may also be practiced in distributed computing
environments where tasks are performed by local and remote
processing devices that are linked (either by hardwired links,
wireless links, or by a combination thereof) through a
communications network. In a distributed computing environment,
program modules may be located in both local and remote memory
storage devices.
[0041] The various embodiments described above are provided by way
of illustration only and should not be construed to limit the scope
of the disclosure. For example, the principles herein can be
applied to set-top phones, mobile phones, or other mobile
communications devices. The predictive contact system components
can reside on a communications device and/or in a communication
network. Those skilled in the art will readily recognize various
modifications and changes that may be made to the principles
described herein without following the example embodiments and
applications illustrated and described herein, and without
departing from the spirit and scope of the disclosure.
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