U.S. patent application number 16/198291 was filed with the patent office on 2019-03-28 for cognitive entity reference recognition.
The applicant listed for this patent is INTERNATIONAL BUSINESS MACHINES CORPORATION. Invention is credited to Jonathan DUNNE, Robert H. GRANT, Jeremy A. GREENBERGER, Trudy L. HEWITT.
Application Number | 20190095421 16/198291 |
Document ID | / |
Family ID | 63105177 |
Filed Date | 2019-03-28 |
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United States Patent
Application |
20190095421 |
Kind Code |
A1 |
DUNNE; Jonathan ; et
al. |
March 28, 2019 |
COGNITIVE ENTITY REFERENCE RECOGNITION
Abstract
Methods, computer program products, and systems are presented.
The methods include, for instance: monitoring one or more message
of the conversation between multiple users for an entity reference;
detecting the entity reference in a message in the conversation. An
entity reference list stores previously established alternate name
referring to a user in the conversation. By analyzing the message
and following messages in the conversation for relevance of and
sentiment to the entity reference, the entity reference is
evaluated and if acceptable, the entity reference list is updated
with the entity reference as a new alternate name to identify the
user in subsequent messages.
Inventors: |
DUNNE; Jonathan; (Dungarvan,
IE) ; GRANT; Robert H.; (Austin, TX) ;
GREENBERGER; Jeremy A.; (Raleigh, NC) ; HEWITT; Trudy
L.; (Cary, NC) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
INTERNATIONAL BUSINESS MACHINES CORPORATION |
Armonk |
NY |
US |
|
|
Family ID: |
63105177 |
Appl. No.: |
16/198291 |
Filed: |
November 21, 2018 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
|
15434279 |
Feb 16, 2017 |
10180937 |
|
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16198291 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G06F 40/30 20200101;
G06F 40/295 20200101; G06N 20/00 20190101; H04L 51/16 20130101;
H04L 67/306 20130101; H04L 51/22 20130101; G06F 40/35 20200101;
H04L 51/32 20130101; H04L 67/22 20130101 |
International
Class: |
G06F 17/27 20060101
G06F017/27; H04L 12/58 20060101 H04L012/58; G06N 99/00 20190101
G06N099/00; H04L 29/08 20060101 H04L029/08 |
Claims
1. A computer implemented method for recognizing a reference to an
entity from a conversation, comprising: monitoring, by one or more
processor, one or more message of the conversation between two or
more users for an entity reference, wherein the two or more users
have respective user names; detecting the entity reference in a
message of the one or more message from the monitoring, the entity
reference being not present in an entity reference list, wherein
the entity reference list includes zero or more alternate name
referring to a user of the two or more users, wherein an alternate
name is distinctive from the respective user names; analyzing the
message and following messages in the conversation for relevance;
determining that the entity reference is appropriate for later use
based on the analyzing; and updating the entity reference list with
the entity reference as a new alternate name for the user such that
the user may be identified by use of the new alternate name in
subsequent messages.
2. The computer implemented method of claim 1, the detecting
comprising: ascertaining that the entity reference refers to a
person based on word classification, analysis of a sentence
structure and/or part of speech of the message.
3. The computer implemented method of claim 1, wherein an entity
reference of the entity reference list is identified by User
attribute instantiated with a user name from a user profile of the
user, wherein the User attribute may be associated with additional
attributes selected from: AltName attribute being instantiated with
the entity reference that identifies the user having the user name
on the user profile; UsedBy attribute being instantiated with
another user name who used the entity reference of the AltName
attribute to refer to the user who has the user name on the user
profile; Group attribute being instantiated with a title of the
conversation to collectively represent all participants of the
conversation; and combinations thereof.
4. The computer implemented method of claim 1, the analyzing
comprising: determining the relevance of the entity reference as
measured by how accurately the user responds to the message in the
following messages; and determining a sentiment of the entity
reference as measured by how positively or negatively the user
responds to the entity reference in the following messages, wherein
the relevance and the sentiment may be obtained by use of various
external services selected from a machine learning process, a
content analytics service, a natural language classification
service, a sentiment analysis service, and combinations
thereof.
5. The computer implemented method of claim 1, the analyzing
further comprising: determining a confidence level of the entity
reference as measured by a number of other comparable entity
references from past messages and conversation records.
6. The computer implemented method of claim 1, further comprising:
discovering that a first user refers to the user by a first
alternate name in a first message of the conversation, wherein the
user, the first user, and a second user participating in the
conversation, and wherein the entity reference list stores the
first alternate name for the user as used by the first user and a
second alternate name for the user as used by the second user; and
displaying, on a device of the second user, the first message with
the second alternate name referring to the user in place of the
first alternate name such that the second user may recognize that
the first message is addressed to the user without knowing that the
first user refers to the user by the first alternate name.
7. The computer implemented method of claim 1, the determining
comprising: ascertaining that the relevance of the entity reference
is medium to high, indicating that the entity reference is
reasonably relevant; and ascertaining that a sentiment of the
entity reference is neutral or positive, indicating that the entity
reference is associated with acceptable sentiment.
8. A computer program product comprising: a computer readable
storage medium readable by one or more processor and storing
instructions for execution by the one or more processor for
performing a method for recognizing a reference to an entity from a
conversation, comprising: monitoring, by one or more processor, one
or more message of the conversation between two or more users for
an entity reference, wherein the two or more users have respective
user names; detecting the entity reference in a message of the one
or more message from the monitoring, the entity reference being not
present in an entity reference list, wherein the entity reference
list includes zero or more alternate name referring to a user of
the two or more users, wherein an alternate name is distinctive
from the respective user names; analyzing the message and following
messages in the conversation for relevance; determining that the
entity reference is appropriate for later use based on the
analyzing; and updating the entity reference list with the entity
reference as a new alternate name for the user such that the user
may be identified by use of the new alternate name in subsequent
messages.
9. The computer program product of claim 8, the detecting
comprising: ascertaining that the entity reference refers to a
person based on word classification, analysis of a sentence
structure and/or part of speech of the message.
10. The computer program product of claim 8, wherein an entity
reference of the entity reference list is identified by User
attribute instantiated with a user name from a user profile of the
user, wherein the User attribute may be associated with additional
attributes selected from: AltName attribute being instantiated with
the entity reference that identifies the user having the user name
on the user profile; UsedBy attribute being instantiated with
another user name who used the entity reference of the AltName
attribute to refer to the user who has the user name on the user
profile; Group attribute being instantiated with a title of the
conversation to collectively represent all participants of the
conversation; and combinations thereof.
11. The computer program product of claim 8, the analyzing
comprising: determining the relevance of the entity reference as
measured by how accurately the user responds to the message in the
following messages; and determining a sentiment of the entity
reference as measured by how positively or negatively the user
responds to the entity reference in the following messages, wherein
the relevance and the sentiment may be obtained by use of various
external services selected from a machine learning process, a
content analytics service, a natural language classification
service, a sentiment analysis service, and combinations
thereof.
12. The computer program product of claim 8, the analyzing further
comprising: determining a confidence level of the entity reference
as measured by a number of other comparable entity references from
past messages and conversation records.
13. The computer program product of claim 8, further comprising:
discovering that a first user refers to the user by a first
alternate name in a first message of the conversation, wherein the
user, the first user, and a second user participating in the
conversation, and wherein the entity reference list stores the
first alternate name for the user as used by the first user and a
second alternate name for the user as used by the second user; and
displaying, on a device of the second user, the first message with
the second alternate name referring to the user in place of the
first alternate name such that the second user may recognize that
the first message is addressed to the user without knowing that the
first user refers to the user by the first alternate name.
14. The computer program product of claim 8, the determining
comprising: ascertaining that the relevance of the entity reference
is medium to high, indicating that the entity reference is
reasonably relevant; and ascertaining that a sentiment of the
entity reference is neutral or positive, indicating that the entity
reference is associated with acceptable sentiment.
15. A system comprising: a memory; one or more processor in
communication with memory; and program instructions executable by
the one or more processor via the memory to perform a method for
recognizing a reference to an entity from a conversation,
comprising: monitoring, by one or more processor, one or more
message of the conversation between two or more users for an entity
reference, wherein the two or more users have respective user
names; detecting the entity reference in a message of the one or
more message from the monitoring, the entity reference being not
present in an entity reference list, wherein the entity reference
list includes zero or more alternate name referring to a user of
the two or more users, wherein an alternate name is distinctive
from the respective user names; analyzing the message and following
messages in the conversation for relevance; determining that the
entity reference is appropriate for later use based on the
analyzing; and updating the entity reference list with the entity
reference as a new alternate name for the user such that the user
may be identified by use of the new alternate name in subsequent
messages.
16. The system of claim 15, the detecting comprising: ascertaining
that the entity reference refers to a person based on word
classification, analysis of a sentence structure and/or part of
speech of the message.
17. The system of claim 15, wherein an entity reference of the
entity reference list is identified by User attribute instantiated
with a user name from a user profile of the user, wherein the User
attribute may be associated with additional attributes selected
from: AltName attribute being instantiated with the entity
reference that identifies the user having the user name on the user
profile; UsedBy attribute being instantiated with another user name
who used the entity reference of the AltName attribute to refer to
the user who has the user name on the user profile; Group attribute
being instantiated with a title of the conversation to collectively
represent all participants of the conversation; and combinations
thereof.
18. The system of claim 15, the analyzing comprising: determining
the relevance of the entity reference as measured by how accurately
the user responds to the message in the following messages;
determining a sentiment of the entity reference as measured by how
positively or negatively the user responds to the entity reference
in the following messages, wherein the relevance and the sentiment
may be obtained by use of various external services selected from a
machine learning process, a content analytics service, a natural
language classification service, a sentiment analysis service, and
combinations thereof; and determining a confidence level of the
entity reference as measured by a number of other comparable entity
references from past messages and conversation records.
19. The system of claim 15, further comprising: discovering that a
first user refers to the user by a first alternate name in a first
message of the conversation, wherein the user, the first user, and
a second user participating in the conversation, and wherein the
entity reference list stores the first alternate name for the user
as used by the first user and a second alternate name for the user
as used by the second user; and displaying, on a device of the
second user, the first message with the second alternate name
referring to the user in place of the first alternate name such
that the second user may recognize that the first message is
addressed to the user without knowing that the first user refers to
the user by the first alternate name.
20. The system of claim 15, the determining comprising:
ascertaining that the relevance of the entity reference is medium
to high, indicating that the entity reference is reasonably
relevant; and ascertaining that a sentiment of the entity reference
is neutral or positive, indicating that the entity reference is
associated with acceptable sentiment.
Description
CROSS-REFERENCE TO RELATED APPLICATION
[0001] This application is a continuation of U.S. application Ser.
No. 15/434,279, filed Feb. 16, 2017, entitled "Cognitive Entity
Reference Recognition", which is incorporated by reference herein
in its entirety.
TECHNICAL FIELD
[0002] The present disclosure relates to cognitive computing
technology, and more particularly to methods, computer program
products, and systems for recognizing names referring to a certain
entity and assisting participants of a conversation with the
name-entity associations.
BACKGROUND
[0003] In conventional group messenger applications, participants
may not recognize alternate names referring to a certain user as
some users may use one alternate name and other users may use
another alternate name. All participants in a group conversation
may not be aware of all nicknames for other participants, which may
be made up during the conversation. Accordingly, users need to
exchange messages only to identify which name refers to whom during
the group conversation.
SUMMARY
[0004] The shortcomings of the prior art are overcome, and
additional advantages are provided, through the provision, in one
aspect, of a method. The method for recognizing a reference to an
entity from a conversation includes, for example: monitoring, by
one or more processor, one or more message of the conversation
between two or more users for an entity reference, wherein the two
or more users have respective user profiles including respective
user names; detecting the entity reference in a message of the one
or more message from the monitoring, the entity reference being not
present in an entity reference list, wherein the entity reference
list includes zero or more alternate name referring to a user of
the two or more users, wherein an alternate name is distinctive
from the respective user names; analyzing the message and following
messages in the conversation for relevance of the entity reference
and sentiment associated with the entity reference; determining
that the entity reference is appropriate for later use based on the
analyzing and respective thresholds for relevance and sentiment;
and updating the entity reference list with the entity reference as
a new alternate name for the user such that the user may be
identified by use of the new alternate name in subsequent
messages.
[0005] Additional features are realized through the techniques set
forth herein. Other embodiments and aspects, including but not
limited to computer program product and system, are described in
detail herein and are considered a part of the claimed
invention.
BRIEF DESCRIPTION OF THE DRAWINGS
[0006] One or more aspects of the present invention are
particularly pointed out and distinctly claimed as examples in the
claims at the conclusion of the specification. The foregoing and
other objects, features, and advantages of the invention are
apparent from the following detailed description taken in
conjunction with the accompanying drawings in which:
[0007] FIG. 1 depicts a system for cognitive entity reference
recognition, in accordance with one or more embodiments set forth
herein;
[0008] FIG. 2 depicts a flowchart performed by the cognitive entity
reference listing process of FIG. 1, in accordance with one or more
embodiments set forth herein;
[0009] FIGS. 3A and 3B depict exemplary conversation messages as
processed by the cognitive entity reference listing process, in
accordance with one or more embodiments set forth herein;
[0010] FIG. 4 depicts a cloud computing node according to an
embodiment of the present invention;
[0011] FIG. 5 depicts a cloud computing environment according to an
embodiment of the present invention; and
[0012] FIG. 6 depicts abstraction model layers according to an
embodiment of the present invention.
DETAILED DESCRIPTION
[0013] FIG. 1 depicts a system 100 for cognitive entity reference
recognition, in accordance with one or more embodiments set forth
herein.
[0014] The system 100 includes a user 101, a user device 110, and
one or more message in a conversation 105. The conversation 105 is
conducted over a telecommunication network amongst one or more
group of users, by use of compatible messaging application programs
running on respective user devices. The telecommunication network
and the messaging application programs are not shown in the system
100, as the conversation 105 may take place by use of conventional
telecommunication environments and messaging tools not within the
scope of this specification. The user device 110 runs a cognitive
entity reference recognition engine 120 that dynamically recognizes
references to users/groups from the conversation 105 and presents
an entity reference adjusted message 199 to the user 101 for a
corresponding message 107 from the one or more message in the
conversation 105.
[0015] The cognitive entity reference recognition engine 120
includes a response catalogue 130, an entity reference list 140, a
query generator 150, a response analyzer 160, and a cognitive
entity reference listing process 170. The cognitive entity
reference recognition engine 120 may employ various external
service utilities including a machine learning process 181, a
content analytics process 183, a natural language classification
process 185, and a sentiment analysis process 187. The machine
learning process 181 may be utilized to create appropriate
responses to a preceding message, as trained by actual conversation
messages. The content analytics process 183 may be utilized to
analyze topics of the conversations, etc. The natural language
classification process 185 may be used to classify the messages as
well as components of the messages as employed by the query
generator 150. The sentiment analysis process 187 may be used to
assess sentiments associated with entity references by the response
analyzer 160, as used by the cognitive entity reference listing
process 170 to evaluate the entity references.
[0016] The response catalogue 130 stores messages that may be
provided responsive to a preceding message in conversations. The
response catalogue 130 may be created by use of, but not limited
to, past messages and conversations, various external service
utilities including the machine learning process 181 and the
content analytics process 183, etc. In certain embodiments of the
present invention, a message of the response catalogues 130 may be
associated with other responses suitable for the preceding message,
respective sentiment associated with each response, the context of
the patterns stored in the response catalogue 130, etc.
[0017] The entity reference list 140 stores one or more entity
names that may be used in displaying the entity names in messages
for the user 101. A user may have a registered user name, a chat
name configured for each conversation thread, or one nickname used
for all conversations, etc., as updated by the cognitive entity
reference listing process 170. The entity reference list 140 stores
the one or more entity names and display the most relevant name for
the participants of the conversation. An exemplary message displays
for respective participants in a same conversation is presented in
FIG. 3B and corresponding description.
[0018] The query generator 150 creates a query upon detecting a new
entity reference appearing in the message 107, which is not stored
in the entity reference list 140. In this specification, the term
"classifier" refers to a group of words that is likely to contain a
name for a person. For example, in a message to a user named
"Robert" stating "Hi, Bob, how are you doing?", "Hi/Bob/you/?" is a
classifier that includes a greeting "Hi", followed by an unknown
term of address "Bob", and followed by the second person pronoun
"you", in an interrogative sentence marked by a question mark (?).
The classifier "Hi/Bob/you/?" signifies that the unknown term of
address "Bob" is likely to indicate the user "Robert" to whom the
message is being addressed. Also in this specification, the term
"query" refers to an inquiry as to if the alternate reference
appearing in the classifier refers to the entity, and includes the
classifier, the user, and an alternate reference to the user. In
the previous example, the query (Classifier, User, AltName) may be
instantiated as (Classifier=hi/Bob/you/?, User=Robert,
AltName=Bob), inquiring if "Bob" refers to the user "Robert" by
user of the classifier "hi . . . you . . . ?", to whom the message
is being addressed. The query generator 150 may utilize one or more
external service utilities, including the content analytics process
183 and the natural language classifier 185, etc.
[0019] The response analyzer 160 analyzes contexts of conversations
and determines whether or not the new entity reference is proper
and relevant within the context of the conversation. Further the
response analyzer 160 may utilize external service utilities such
as the sentiment analysis process 187 to assess sentiment
associated with the response in using the new entity reference.
[0020] The cognitive entity reference listing process 170 passively
monitors messages of the conversation 105, detects a new entity
reference from the messages, and processes the entity reference by
coordinating and controlling operations of the query generator 150
and the response analyzer 160, and by utilizing and updating data
stored in the response catalogue 130 and the entity reference list
140. Detailed process of the cognitive entity reference listing
process 170 is presented in FIG. 2 and corresponding
description.
[0021] While using messaging services for groups, participants in a
group conversation may use various names to refer to one user. The
group conversation is ordinarily conducted by use of group chat
applications and/or group text messengers. Over time, the names may
evolve to other names and if the participants do not follow the
group conversation closely, it would be very difficult for such
infrequent participants to understand who is talking about whom.
Similarly, wherein one user is called by many names, participants
of the group conversation may not understand who is addressed by
what name, may forget some names while remembering others, etc.,
and the group conversation may be frequently interrupted due to the
inconsistency in the terms of address amongst the participants.
Conventional messaging applications uses rudimentary manual setting
of one or more chat name, which does not reflect the terms of
address for respective users in the ongoing conversation.
[0022] Certain embodiments of the present invention dynamically
recognizes terms of address associated with entities from
conversation messages as entity references based on relevance and
semantics of the language, analyzes the sentiment accompanying the
entity references, and registers the entity references that are
positively accepted for future usage. Further, based on the
characterization of the groups, certain alternate names may be
further evaluated for the appropriateness. For example, for a group
conversation amongst high school friends, rather informal nicknames
for one user may be evaluated as acceptable, while the same
informal nicknames for the same user may be evaluated as
inappropriate for another group conversation amongst work
colleagues.
[0023] FIG. 2 depicts a flowchart performed by the cognitive entity
reference listing process 170 of FIG. 1, in accordance with one or
more embodiments set forth herein.
[0024] Each of blocks 210 through 250 is a predefined process, in
which multiple operations may be performed to achieve respective
functionalities. In preparation of running the cognitive entity
reference listing process 170, a user profile including user name
information had been configured either manually by a user or
automatically by the cognitive entity reference recognition engine
120 for each user.
[0025] In block 210, the cognitive entity reference listing process
170 monitors messages of a conversation in real-time and detects a
new reference to an entity and use contexts of the new reference in
a message by analyzing individual messages of the conversation. As
entities in the conversation are people and people refer to other
people by respective names, the references to entities may be names
of participants in the conversation, and may be detected by natural
language classification on the subject word, sentence structure
analysis as well as part of speech analysis, including but not
limited to positions relative to, greetings, punctuations,
beginning and/or end of a sentence, etc. If one of the participants
in the conversation refers to another participant by using an
alternate name other than what is already known to other
participants, as stored in the entity reference list 140, the
cognitive entity reference listing process 170 may detect such
alternate name. A participant may belong to multiple conversation
group based on the relationships amongst members of each
conversation group, in which the participant may be referred to by
distinctive names. For example, a user may have a respective
conversation thread with family members who call the user "Eddy",
with colleagues who call the user "Ed", and with bowling teammates
who call the user "Little Ed", etc. The cognitive entity reference
listing process 170 detects the alternate reference as a new
reference if the alternate reference is not discovered in the
entity reference list 140, in which the new reference would be
stored, as resulting from block 250. Exemplary usage of multiple
alternate references to one entity is presented in FIG. 3B and
corresponding description. Then the cognitive entity reference
listing process 170 proceeds with block 220.
[0026] In block 220, the cognitive entity reference listing process
170 creates a query for an alternative name detected in block 210
and initiates analysis of subsequent messages in the conversation.
The query for the alternate name may include a classifier and
additional attributes, including the alternate name and/or usage
context of the alternate name. Then the cognitive entity reference
listing process 170 proceeds with block 230.
[0027] In certain embodiment of the present invention, the
cognitive entity reference listing process 170 may create the query
based on the context of messages in the conversation, even the
alternate name had not been detected in block 210 because the
alternate name was not a typical entity reference such as a proper
noun and/or other candidate words/phrases. In certain embodiment of
the present invention, the classifier of the query may be obtained
by an external service utility such as a natural language
classifier. For example, a query having a classifier
"hey/Rob/you/?" and AltName "Rob" may be created based on a message
from Nathan to Robert stating "Hey, Rob, can you call me?" for
"User=Robert". In the same example, the query may further include
usage contexts representing circumstances in which the alternative
name "Rob" is used for the user "Robert", such as a specific group
of participants in a conversation, a particular user who used a
specific alternative name, etc. Accordingly, the query may further
include "Attribute:Group=ABC High" as Robert and Nathan are in a
group chat titled ABC High, "Attribute:UsedBy=Nathan" as Nathan
used the AltName "Rob", and/or additional alternative name and
usage contexts that had been previously detected as an alternative
reference to the user Robert and stored in the entity reference
list.
[0028] In block 230, the cognitive entity reference listing process
170 evaluates the entity reference detected in block 210 based on
the subsequent message in the conversation, as analyzed from block
220. Then the cognitive entity reference listing process 170
proceeds with block 240.
[0029] In certain embodiments of the present invention, the entity
reference may be evaluated for relevance, sentiment, confidence,
and combinations thereof, wherein the relevance indicates how
accurately the entity reference designates an entity participating
in the conversation and conversely may be measured by how
accurately the user responds to the message in subsequent messages
in the conversation. In the same embodiment of the present
invention, the sentiment indicates how positively or negatively the
entity reference is perceived by the entity being designated and/or
other participants of the conversation, which may be measured by
examining how positively or negatively the user responds to the
entity reference in the following messages. In the same embodiment
of the present invention, the confidence indicates how confident
the participants may be on referring the entity by the entity
reference based on the number of comparable references. For
example, the cognitive entity reference listing process 170 may
assign a very high level of confidence on an "AltName=Ed", or
"AltName=Eddy" for "User=Edward", but would assign a very low level
of confidence on an "AltName=Jack" for "User=Edward", based on the
number of entity references in historical data for past
conversations. In certain embodiments of the present invention, the
cognitive entity reference listing process 170 evaluates the entity
reference in two stages, first by relevance and next by sentiment,
such that the entity reference may be reasonably accurate in
referring to the entity and the reference is reusable as being
positively perceived. Exemplary entity reference evaluation is
described in FIG. 3A and corresponding description.
[0030] In block 240, the cognitive entity reference listing process
170 determines whether or not the new entity reference detected
from block 210 is acceptable such that the new entity reference may
be reused to refer to the entity in later messages of the same
conversation thread as well as other conversations. In certain
embodiments of the present invention, the cognitive entity
reference listing process 170 may determine the acceptability of
the new entity reference by use of, inter alia, a respective range
of applicable metric values, respective thresholds for cut-off for
each applicable metrics, etc., depending on the methods of
evaluation from block 230. For example, if the relevance of the
entity reference is quantified as a score during evaluation of
block 230, then the acceptability test may be a range of relevance
scores. Also in the same example, the relevance of the entity
reference may be classified in one of three levels including low,
medium, and high, indicating medium to high level of relevance to
be acceptable. For another example, if the sentiment of the entity
reference is evaluated as one of three categories including
positive, neutral, and negative, then acceptability test may be a
cut-off for the entity reference associated with a negative
sentiment, and the entity reference associated with either positive
or neutral sentiment may be acceptable. Sentiments may be evaluated
for the new entity reference on the message wherein the new entity
reference first appeared, subsequent messages responding to the new
entity reference, and/or any comment on the new entity reference
within the group conversation, etc. If the cognitive entity
reference listing process 170 determines that the new entity
reference is acceptable, then the cognitive entity reference
listing process 170 proceeds with block 250. If the cognitive
entity reference listing process 170 determines that the new entity
reference is not acceptable, then the cognitive entity reference
listing process 170 concludes processing the new entity reference
detected in block 210.
[0031] In block 250, the cognitive entity reference listing process
170 updates the entity reference list 140 with the new entity
reference such that the new entity reference may be reused to
identify the entity as an alternate name, as established in blocks
220 through 240. Exemplary usage of multiple alternate references
to one entity is presented in FIG. 3B and corresponding
description. The user 101 may manually remove or edit entity
references in the entity reference list 140, and/or may turn on and
turn off the cognitive entity reference recognition engine 120 on
the user device 110. Also the alternate references may be
distinctive for respective groups and/or conversations for the same
user, and based on analyzing participants and known entity
references of the participants, entity references may be
transferred from other entity reference lists of the participants.
In certain embodiments of the present invention, updated entity
reference list 140 may be synchronized with other entity reference
lists on user devices of respective participants of the same group
conversation. Then the cognitive entity reference listing process
170 concludes processing the new entity reference detected in block
210. The cognitive entity reference listing process 170 iterates
blocks 210 through 250 as a unit while passively monitoring
messages in conversations.
[0032] FIGS. 3A and 3B depict exemplary conversation messages as
processed by the cognitive entity reference listing process 170, in
accordance with one or more embodiments set forth herein.
[0033] Prior to running the cognitive entity reference recognition
engine 120 application program, both Robert and Nathan had filled
out respective user name information in the respective user profile
for the cognitive entity reference recognition engine 120
application program running on the respective user devices. Both
users may have configured the respective entity reference lists
that catalogues nicknames for people in the conversations. The
cognitive reference recognition engine 120 application program may
run as an add-on and/or plug-in program for conventional messaging
applications.
[0034] In the conversation of 301 and 302 of FIG. 3A, Robert and
Nathan are participating in a group chat for Group A of close
friends. In message 301, Nathan asks if Robert wants to join them
in plans for the evening. Boldfaced "Rob" in message 301 indicates
a nickname of Robert used in the group chat A. If the cognitive
entity reference listing process 170 detects "Rob" in message 301
for the first time, the cognitive entity reference listing process
170 generates a query if "Rob" is a usable nickname for the user
"Robert" and proceeds with analyzing message 302 to see how the
alternate name "Rob" is responded. In message 302, the cognitive
entity reference listing process 170 extracts "Hey", "Ya", and "I",
and evaluates the alternate name "Rob" as relevant to "Robert" as
well as associated with an positive/neutral sentiment based on
"Hey/Ya/I" in a declarative statement, which properly responds to
message 301 as if the registered user name "Robert" has been used.
Accordingly, the cognitive entity reference listing process 170
updates the entity reference list with the nickname
"Attribute:AltName=Rob" for "User=Robert" as "Attribute:
UsedBy=Nathan" or "Attribute:UsedBy=Group A".
[0035] Further in the conversation of 311 through 313 of FIG. 3A,
Robert and Nathan are exchanging messages, following the
conversation of 301 and 302. In message 311, Nathan addresses
Robert with a nickname "Rob", which had been previously registered
as above. In message 312, Robert uses a new term of address
"Natedog" in responding to message 311 as well as asking another
question "Hey/Natedog/yes; Are/you/?". As in analyzing message 302,
the cognitive entity reference listing process 170 detects
"Natedog", generates a query if "Natedog" is a usable nickname for
the user "Nathan" and proceeds with analyzing message 313 to see
how the new alternate name "Natedog" is responded. In message 313,
the cognitive entity reference listing process 170 extracts "Yes",
"you", and "great", and evaluates the alternate name "Natedog" as
relevant to "Nathan" as well as associated with an positive/neutral
sentiment based on "Yes/great" used in a declarative statement,
which properly responds to message 312 as if the registered user
name "Nathan" has been used. Accordingly, the cognitive entity
reference listing process 170 updates the entity reference list
with the nickname "Attribute:AltName=Natedog" for "User=Nathan" as
"Attribute: UsedBy=Robert". Further, the cognitive entity reference
listing process 170 may be configured to permit all participants in
the same group chat to use the nickname in the entity reference
list without further evaluation, as the group chat members may
share similar recognition and sentiment for the nickname as other
participants.
[0036] In another conversation having message 320 of FIG. 3B,
Robert, Mark, and Jane are exchanging messages, in another group
chat for a book club. Jane and Robert are college friends and,
because Robert goes by "Rob" amongst the college friends, Jane
calls Robert Rob. Mark and Robert are work colleagues, and, because
Robert goes by Bob at work, Mark calls Robert Bob. Both nicknames
"Rob" and "Bob" are stored in the entity reference list for
"Robert" in respective user devices for Jane and Mark. In message
320, Jane is asking Robert a question by calling Robert by one of
the nicknames "Rob". On the user device of Jane, message 320J with
the nickname "Rob" is displayed as Jane used. On the user device of
Robert, message 320R with the nickname "Rob" is displayed as Jane
used such that Robert would know that Jane asked the question. On
the user device of Mark, message 320M with the nickname "Bob" is
displayed because Mark is not familiar with Robert going by "Rob"
and Mark may be confused with the nickname "Rob".
[0037] Certain embodiments of the present invention may offer
various technical computing advantages in assisting users,
including automated cognitive entity reference detection and
customized replacement for respective users in group conversation
messages. Multiple terms of address for a user in a group
conversation may be stored in an entity reference list on
respective user devices and synchronized for participants of the
same group conversation such that the recorded entity references
would be recognized as nicknames for the user later. A new entity
reference would be detected by content analytics and natural
language classification of words and/or messages forming a context
for the entity reference. The new entity reference is evaluated by
relevance, confidence, and sentiment associated with the entity
reference such that only a relevant and associated with a positive
sentiment would be accepted for repeated usage. Certain embodiments
of the present invention improves efficiency and accuracy in
identifying one user by numerous alternate names, as well as
presents individualized display of alternate names pursuant to
familiarity of individual users for respective names. Certain
embodiments of the present invention assists participants of a
group conversation by preventing confusion as to the association of
certain alternate names and respective participants and by
preventing repetitive exchange of messages to identify who is
referred to by what name, resulting in reduced and more efficient
use of network traffics. Certain embodiments of the present
invention may be implemented as an add-on or plug-in features of
conventional messenger applications such that more focused and
efficient cognitive entity reference recognition and processing
would be available than in messenger applications with integrated
entity reference recognition functionalities.
[0038] FIGS. 4-6 depict various aspects of computing, including a
computer system and cloud computing, in accordance with one or more
aspects set forth herein.
[0039] It is understood in advance that although this disclosure
includes a detailed description on cloud computing, implementation
of the teachings recited herein are not limited to a cloud
computing environment. Rather, embodiments of the present invention
are capable of being implemented in conjunction with any other type
of computing environment now known or later developed.
[0040] Cloud computing is a model of service delivery for enabling
convenient, on-demand network access to a shared pool of
configurable computing resources (e.g. networks, network bandwidth,
servers, processing, memory, storage, applications, virtual
machines, and services) that can be rapidly provisioned and
released with minimal management effort or interaction with a
provider of the service. This cloud model may include at least five
characteristics, at least three service models, and at least four
deployment models.
[0041] Characteristics are as follows:
[0042] On-demand self-service: a cloud consumer can unilaterally
provision computing capabilities, such as server time and network
storage, as needed automatically without requiring human
interaction with the service's provider.
[0043] Broad network access: capabilities are available over a
network and accessed through standard mechanisms that promote use
by heterogeneous thin or thick client platforms (e.g., mobile
phones, laptops, and PDAs).
[0044] Resource pooling: the provider's computing resources are
pooled to serve multiple consumers using a multi-tenant model, with
different physical and virtual resources dynamically assigned and
reassigned according to demand. There is a sense of location
independence in that the consumer generally has no control or
knowledge over the exact location of the provided resources but may
be able to specify location at a higher level of abstraction (e.g.,
country, state, or datacenter).
[0045] Rapid elasticity: capabilities can be rapidly and
elastically provisioned, in some cases automatically, to quickly
scale out and rapidly released to quickly scale in. To the
consumer, the capabilities available for provisioning often appear
to be unlimited and can be purchased in any quantity at any
time.
[0046] Measured service: cloud systems automatically control and
optimize resource use by leveraging a metering capability at some
level of abstraction appropriate to the type of service (e.g.,
storage, processing, bandwidth, and active user accounts). Resource
usage can be monitored, controlled, and reported providing
transparency for both the provider and consumer of the utilized
service.
[0047] Service Models are as follows:
[0048] Software as a Service (SaaS): the capability provided to the
consumer is to use the provider's applications running on a cloud
infrastructure. The applications are accessible from various client
devices through a thin client interface such as a web browser
(e.g., web-based e-mail). The consumer does not manage or control
the underlying cloud infrastructure including network, servers,
operating systems, storage, or even individual application
capabilities, with the possible exception of limited user-specific
application configuration settings.
[0049] Platform as a Service (PaaS): the capability provided to the
consumer is to deploy onto the cloud infrastructure
consumer-created or acquired applications created using programming
languages and tools supported by the provider. The consumer does
not manage or control the underlying cloud infrastructure including
networks, servers, operating systems, or storage, but has control
over the deployed applications and possibly application hosting
environment configurations.
[0050] Infrastructure as a Service (IaaS): the capability provided
to the consumer is to provision processing, storage, networks, and
other fundamental computing resources where the consumer is able to
deploy and run arbitrary software, which can include operating
systems and applications. The consumer does not manage or control
the underlying cloud infrastructure but has control over operating
systems, storage, deployed applications, and possibly limited
control of select networking components (e.g., host firewalls).
[0051] Deployment Models are as follows:
[0052] Private cloud: the cloud infrastructure is operated solely
for an organization. It may be managed by the organization or a
third party and may exist on-premises or off-premises.
[0053] Community cloud: the cloud infrastructure is shared by
several organizations and supports a specific community that has
shared concerns (e.g., mission, security requirements, policy, and
compliance considerations). It may be managed by the organizations
or a third party and may exist on-premises or off-premises.
[0054] Public cloud: the cloud infrastructure is made available to
the general public or a large industry group and is owned by an
organization selling cloud services.
[0055] Hybrid cloud: the cloud infrastructure is a composition of
two or more clouds (private, community, or public) that remain
unique entities but are bound together by standardized or
proprietary technology that enables data and application
portability (e.g., cloud bursting for load-balancing between
clouds).
[0056] A cloud computing environment is service oriented with a
focus on statelessness, low coupling, modularity, and semantic
interoperability. At the heart of cloud computing is an
infrastructure comprising a network of interconnected nodes.
[0057] Referring now to FIG. 4, a schematic of an example of a
computer system/cloud computing node is shown. Cloud computing node
10 is only one example of a suitable cloud computing node and is
not intended to suggest any limitation as to the scope of use or
functionality of embodiments of the invention described herein.
Regardless, cloud computing node 10 is capable of being implemented
and/or performing any of the functionality set forth
hereinabove.
[0058] In cloud computing node 10 there is a computer system 12,
which is operational with numerous other general purpose or special
purpose computing system environments or configurations. Examples
of well-known computing systems, environments, and/or
configurations that may be suitable for use with computer system 12
include, but are not limited to, personal computer systems, server
computer systems, thin clients, thick clients, hand-held or laptop
devices, multiprocessor systems, microprocessor-based systems, set
top boxes, programmable consumer electronics, network PCs,
minicomputer systems, mainframe computer systems, and distributed
cloud computing environments that include any of the above systems
or devices, and the like.
[0059] Computer system 12 may be described in the general context
of computer system-executable instructions, such as program
processes, being executed by a computer system. Generally, program
processes may include routines, programs, objects, components,
logic, data structures, and so on that perform particular tasks or
implement particular abstract data types. Computer system 12 may be
practiced in distributed cloud computing environments where tasks
are performed by remote processing devices that are linked through
a communications network. In a distributed cloud computing
environment, program processes may be located in both local and
remote computer system storage media including memory storage
devices.
[0060] As shown in FIG. 4, computer system 12 in cloud computing
node 10 is shown in the form of a general-purpose computing device.
The components of computer system 12 may include, but are not
limited to, one or more processors 16, a system memory 28, and a
bus 18 that couples various system components including system
memory 28 to processor 16.
[0061] Bus 18 represents one or more of any of several types of bus
structures, including a memory bus or memory controller, a
peripheral bus, an accelerated graphics port, and a processor or
local bus using any of a variety of bus architectures. By way of
example, and not limitation, such architectures include Industry
Standard Architecture (ISA) bus, Micro Channel Architecture (MCA)
bus, Enhanced ISA (EISA) bus, Video Electronics Standards
Association (VESA) local bus, and Peripheral Component
Interconnects (PCI) bus.
[0062] Computer system 12 typically includes a variety of computer
system readable media. Such media may be any available media that
is accessible by computer system 12, and it includes both volatile
and non-volatile media, removable and non-removable media.
[0063] System memory 28 can include computer system readable media
in the form of volatile memory, such as random access memory (RAM)
30 and/or cache memory 32. Computer system 12 may further include
other removable/non-removable, volatile/non-volatile computer
system storage media. By way of example only, storage system 34 can
be provided for reading from and writing to a non-removable,
non-volatile magnetic media (not shown and typically called a "hard
drive"). Although not shown, a magnetic disk drive for reading from
and writing to a removable, non-volatile magnetic disk (e.g., a
"floppy disk"), and an optical disk drive for reading from or
writing to a removable, non-volatile optical disk such as a CD-ROM,
DVD-ROM or other optical media can be provided. In such instances,
each can be connected to bus 18 by one or more data media
interfaces. As will be further depicted and described below, memory
28 may include at least one program product having a set (e.g., at
least one) of program processes that are configured to carry out
the functions of embodiments of the invention.
[0064] One or more program 40, having a set (at least one) of
program processes 42, may be stored in memory 28 by way of example,
and not limitation, as well as an operating system, one or more
application programs, other program processes, and program data.
Each of the operating system, one or more application programs,
other program processes, and program data or some combination
thereof, may include an implementation of the cognitive entity
reference recognition engine 120 including the cognitive entity
reference listing process 170 of FIG. 1. Program processes 42, as
in the cognitive entity reference recognition engine 120 generally
carry out the functions and/or methodologies of embodiments of the
invention as described herein.
[0065] Computer system 12 may also communicate with one or more
external devices 14 such as a keyboard, a pointing device, a
display 24, etc.; one or more devices that enable a user to
interact with computer system 12; and/or any devices (e.g., network
card, modem, etc.) that enable computer system 12 to communicate
with one or more other computing devices. Such communication can
occur via Input/Output (I/O) interfaces 22. Still yet, computer
system 12 can communicate with one or more networks such as a local
area network (LAN), a general wide area network (WAN), and/or a
public network (e.g., the Internet) via network adapter 20. As
depicted, network adapter 20 communicates with the other components
of computer system 12 via bus 18. It should be understood that
although not shown, other hardware and/or software components could
be used in conjunction with computer system 12. Examples, include,
but are not limited to: microcode, device drivers, redundant
processors, external disk drive arrays, RAID systems, tape drives,
and data archival storage systems, etc.
[0066] Referring now to FIG. 5, illustrative cloud computing
environment 50 is depicted. As shown, cloud computing environment
50 comprises one or more cloud computing nodes 10 with which local
computing devices used by cloud consumers, such as, for example,
personal digital assistant (PDA) or cellular telephone 54A, desktop
computer 54B, laptop computer 54C, and/or automobile computer
system 54N may communicate. Nodes 10 running the cognitive entity
reference recognition engine 120 of FIG. 1 may communicate with one
another. They may be grouped (not shown) physically or virtually,
in one or more networks, such as Private, Community, Public, or
Hybrid clouds as described hereinabove, or a combination thereof.
This allows cloud computing environment 50 to offer infrastructure,
platforms and/or software as services for which a cloud consumer
does not need to maintain resources on a local computing device. It
is understood that the types of computing devices 54A-N shown in
FIG. 5 are intended to be illustrative only and that computing
nodes 10 and cloud computing environment 50 can communicate with
any type of computerized device over any type of network and/or
network addressable connection (e.g., using a web browser).
[0067] Referring now to FIG. 6, a set of functional abstraction
layers provided by cloud computing environment 50 (FIG. 5) is
shown. It should be understood in advance that the components,
layers, and functions shown in FIG. 6 are intended to be
illustrative only and embodiments of the invention are not limited
thereto. As depicted, the following layers and corresponding
functions are provided:
[0068] Hardware and software layer 60 includes hardware and
software components. Examples of hardware components include:
mainframes 61; RISC (Reduced Instruction Set Computer) architecture
based servers 62; servers 63; blade servers 64; storage devices 65;
and networks and networking components 66. In some embodiments,
software components include network application server software 67
and database software 68.
[0069] Virtualization layer 70 provides an abstraction layer from
which the following examples of virtual entities may be provided:
virtual servers 71; virtual storage 72; virtual networks 73,
including virtual private networks; virtual applications and
operating systems 74; and virtual clients 75.
[0070] In one example, management layer 80 may provide the
functions described below. Resource provisioning 81 provides
dynamic procurement of computing resources and other resources that
are utilized to perform tasks within the cloud computing
environment. Metering and Pricing 82 provide cost tracking as
resources are utilized within the cloud computing environment, and
billing or invoicing for consumption of these resources. In one
example, these resources may comprise application software
licenses. Security provides identity verification for cloud
consumers and tasks, as well as protection for data and other
resources. User portal 83 provides access to the cloud computing
environment for consumers and system administrators. Service level
management 84 provides cloud computing resource allocation and
management such that required service levels are met. Service Level
Agreement (SLA) planning and fulfillment 85 provide pre-arrangement
for, and procurement of, cloud computing resources for which a
future requirement is anticipated in accordance with an SLA.
[0071] Workloads layer 90 provides examples of functionality for
which the cloud computing environment may be utilized. Examples of
workloads and functions which may be provided from this layer
include: mapping and navigation 91; software development and
lifecycle management 92; virtual classroom education delivery 93;
data analytics processing 94; transaction processing 95; and
processing components for the cognitive entity reference
recognition engine 96, as described herein. The processing
components 96 can be understood as one or more program 40 described
in FIG. 4.
[0072] The present invention may be a system, a method, and/or a
computer program product. The computer program product may include
a computer readable storage medium (or media) having computer
readable program instructions thereon for causing a processor to
carry out aspects of the present invention.
[0073] The computer readable storage medium can be a tangible
device that can retain and store instructions for use by an
instruction execution device. The computer readable storage medium
may be, for example, but is not limited to, an electronic storage
device, a magnetic storage device, an optical storage device, an
electromagnetic storage device, a semiconductor storage device, or
any suitable combination of the foregoing. A non-exhaustive list of
more specific examples of the computer readable storage medium
includes the following: a portable computer diskette, a hard disk,
a random access memory (RAM), a read-only memory (ROM), an erasable
programmable read-only memory (EPROM or Flash memory), a static
random access memory (SRAM), a portable compact disc read-only
memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a
floppy disk, a mechanically encoded device such as punch-cards or
raised structures in a groove having instructions recorded thereon,
and any suitable combination of the foregoing. A computer readable
storage medium, as used herein, is not to be construed as being
transitory signals per se, such as radio waves or other freely
propagating electromagnetic waves, electromagnetic waves
propagating through a waveguide or other transmission media (e.g.,
light pulses passing through a fiber-optic cable), or electrical
signals transmitted through a wire.
[0074] Computer readable program instructions described herein can
be downloaded to respective computing/processing devices from a
computer readable storage medium or to an external computer or
external storage device via a network, for example, the Internet, a
local area network, a wide area network and/or a wireless network.
The network may comprise copper transmission cables, optical
transmission fibers, wireless transmission, routers, firewalls,
switches, gateway computers and/or edge servers. A network adapter
card or network interface in each computing/processing device
receives computer readable program instructions from the network
and forwards the computer readable program instructions for storage
in a computer readable storage medium within the respective
computing/processing device.
[0075] Computer readable program instructions for carrying out
operations of the present invention may be assembler instructions,
instruction-set-architecture (ISA) instructions, machine
instructions, machine dependent instructions, microcode, firmware
instructions, state-setting data, or either source code or object
code written in any combination of one or more programming
languages, including an object oriented programming language such
as Smalltalk, C++ or the like, and conventional procedural
programming languages, such as the "C" programming language or
similar programming languages. The computer readable program
instructions may execute entirely on the user's computer, partly on
the user's computer, as a stand-alone software package, partly on
the user's computer and partly on a remote computer or entirely on
the remote computer or server. In the latter scenario, the remote
computer may be connected to the user's computer through any type
of network, including a local area network (LAN) or a wide area
network (WAN), or the connection may be made to an external
computer (for example, through the Internet using an Internet
Service Provider). In some embodiments, electronic circuitry
including, for example, programmable logic circuitry,
field-programmable gate arrays (FPGA), or programmable logic arrays
(PLA) may execute the computer readable program instructions by
utilizing state information of the computer readable program
instructions to personalize the electronic circuitry, in order to
perform aspects of the present invention.
[0076] Aspects of the present invention are described herein with
reference to flowchart illustrations and/or block diagrams of
methods, apparatus (systems), and computer program products
according to embodiments of the invention. It will be understood
that each block of the flowchart illustrations and/or block
diagrams, and combinations of blocks in the flowchart illustrations
and/or block diagrams, can be implemented by computer readable
program instructions.
[0077] These computer readable program instructions may be provided
to a processor of a general purpose computer, special purpose
computer, or other programmable data processing apparatus to
produce a machine, such that the instructions, which execute via
the processor of the computer or other programmable data processing
apparatus, create means for implementing the functions/acts
specified in the flowchart and/or block diagram block or blocks.
These computer readable program instructions may also be stored in
a computer readable storage medium that can direct a computer, a
programmable data processing apparatus, and/or other devices to
function in a particular manner, such that the computer readable
storage medium having instructions stored therein comprises an
article of manufacture including instructions which implement
aspects of the function/act specified in the flowchart and/or block
diagram block or blocks.
[0078] The computer readable program instructions may also be
loaded onto a computer, other programmable data processing
apparatus, or other device to cause a series of operational steps
to be performed on the computer, other programmable apparatus or
other device to produce a computer implemented process, such that
the instructions which execute on the computer, other programmable
apparatus, or other device implement the functions/acts specified
in the flowchart and/or block diagram block or blocks.
[0079] The flowchart and block diagrams in the Figures illustrate
the architecture, functionality, and operation of possible
implementations of systems, methods, and computer program products
according to various embodiments of the present invention. In this
regard, each block in the flowchart or block diagrams may represent
a module, segment, or portion of instructions, which comprises one
or more executable instructions for implementing the specified
logical function(s). In some alternative implementations, the
functions noted in the block may occur out of the order noted in
the figures. For example, two blocks shown in succession may, in
fact, be executed substantially concurrently, or the blocks may
sometimes be executed in the reverse order, depending upon the
functionality involved. It will also be noted that each block of
the block diagrams and/or flowchart illustration, and combinations
of blocks in the block diagrams and/or flowchart illustration, can
be implemented by special purpose hardware-based systems that
perform the specified functions or acts or carry out combinations
of special purpose hardware and computer instructions.
[0080] The terminology used herein is for the purpose of describing
particular embodiments only and is not intended to be limiting. As
used herein, the singular forms "a," "an," and "the" are intended
to include the plural forms as well, unless the context clearly
indicates otherwise. It will be further understood that the terms
"comprise" (and any form of comprise, such as "comprises" and
"comprising"), "have" (and any form of have, such as "has" and
"having"), "include" (and any form of include, such as "includes"
and "including"), and "contain" (and any form of contain, such as
"contains" and "containing") are open-ended linking verbs. As a
result, a method or device that "comprises," "has," "includes," or
"contains" one or more steps or elements possesses those one or
more steps or elements, but is not limited to possessing only those
one or more steps or elements. Likewise, a step of a method or an
element of a device that "comprises," "has," "includes," or
"contains" one or more features possesses those one or more
features, but is not limited to possessing only those one or more
features. Furthermore, a device or structure that is configured in
a certain way is configured in at least that way, but may also be
configured in ways that are not listed.
[0081] The corresponding structures, materials, acts, and
equivalents of all means or step plus function elements in the
claims below, if any, are intended to include any structure,
material, or act for performing the function in combination with
other claimed elements as specifically claimed. The description set
forth herein has been presented for purposes of illustration and
description, but is not intended to be exhaustive or limited to the
form disclosed. Many modifications and variations will be apparent
to those of ordinary skill in the art without departing from the
scope and spirit of the disclosure. The embodiment was chosen and
described in order to best explain the principles of one or more
aspects set forth herein and the practical application, and to
enable others of ordinary skill in the art to understand one or
more aspects as described herein for various embodiments with
various modifications as are suited to the particular use
contemplated.
* * * * *