U.S. patent application number 14/992273 was filed with the patent office on 2017-05-11 for electronic meeting intelligence.
This patent application is currently assigned to RICOH COMPANY, LTD.. The applicant listed for this patent is Charchit Arora, Hiroshi Kitada, Steven A. Nelson, Lana Wong. Invention is credited to Charchit Arora, Hiroshi Kitada, Steven A. Nelson, Lana Wong.
Application Number | 20170134446 14/992273 |
Document ID | / |
Family ID | 57396252 |
Filed Date | 2017-05-11 |
United States Patent
Application |
20170134446 |
Kind Code |
A1 |
Kitada; Hiroshi ; et
al. |
May 11, 2017 |
Electronic Meeting Intelligence
Abstract
Techniques related to electronic meeting intelligence are
disclosed. An apparatus receives audio/video data including first
meeting content data for an electronic meeting that includes
multiple participants. The apparatus extracts the first meeting
content data from the audio/video data. The apparatus generates
meeting content metadata based on analyzing the first meeting
content data. The apparatus includes the meeting content metadata
in a report of the electronic meeting. If the apparatus determines
that the audio/video data includes a cue for the apparatus to
intervene in the electronic meeting, the apparatus generates
intervention data including second meeting content data that is
different from the first meeting content data. During the
electronic meeting, the apparatus sends the intervention data to
one or more nodes associated with at least one participant of the
multiple participants.
Inventors: |
Kitada; Hiroshi; (Tuckahoe,
NY) ; Nelson; Steven A.; (San Jose, CA) ;
Wong; Lana; (Belleville, NJ) ; Arora; Charchit;
(Sunnyvale, CA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Kitada; Hiroshi
Nelson; Steven A.
Wong; Lana
Arora; Charchit |
Tuckahoe
San Jose
Belleville
Sunnyvale |
NY
CA
NJ
CA |
US
US
US
US |
|
|
Assignee: |
RICOH COMPANY, LTD.
TOKYO
JP
|
Family ID: |
57396252 |
Appl. No.: |
14/992273 |
Filed: |
January 11, 2016 |
Related U.S. Patent Documents
|
|
|
|
|
|
Application
Number |
Filing Date |
Patent Number |
|
|
62253325 |
Nov 10, 2015 |
|
|
|
Current U.S.
Class: |
1/1 |
Current CPC
Class: |
H04L 12/1822 20130101;
G10L 25/57 20130101; H04L 12/1831 20130101; H04L 65/1083 20130101;
H04N 7/152 20130101; H04L 65/403 20130101; G06Q 10/1095 20130101;
G10L 25/63 20130101; H04M 7/0027 20130101; H04N 7/15 20130101; G06Q
10/10 20130101; H04M 3/567 20130101; H04M 2201/40 20130101 |
International
Class: |
H04L 29/06 20060101
H04L029/06; G10L 25/57 20060101 G10L025/57; H04N 7/15 20060101
H04N007/15; G10L 25/63 20060101 G10L025/63 |
Claims
1. An apparatus comprising: one or more processors; and one or more
non-transitory computer-readable media storing instructions which,
when processed by the one or more processors, cause: receiving, at
the apparatus, audio/video data comprising first meeting content
data for an electronic meeting that includes a plurality of
participants; determining, by the apparatus, that the audio/video
data comprising first meeting content data for an electronic
meeting that includes a plurality of participants comprises a cue
for the apparatus to intervene in the electronic meeting, wherein
determining, by the apparatus, that the audio/video data comprises
a cue for the apparatus to intervene in the electronic meeting
includes determining, by the apparatus, that the audio/video data
is related to a particular agenda topic for the electronic meeting;
in response to the cue to intervene in the electronic meeting,
generating, by the apparatus, intervention data comprising second
meeting content data that is different from the first meeting
content data and indicates that the audio/video data is related to
the particular agenda topic for the electronic meeting; during the
electronic meeting, sending the intervention data comprising second
meeting content data that is different from the first meeting
content data and indicates that the audio/video data is related to
the particular agenda topic for the electronic meeting, from the
apparatus to one or more nodes associated with at least one
participant of the plurality of participants, wherein processing of
the second meeting content data at the one or more nodes associated
with at least one participant of the plurality of participants
provides a visual indication of the particular agenda topic.
2. The apparatus of claim 1, wherein determining, by the apparatus,
that the audio/video data is related to the particular agenda topic
for the electronic meeting includes performing speech or text
recognition on the audio/video data.
3. The apparatus of claim 1, wherein determining, by the apparatus,
that the audio/video data comprises a cue for the apparatus to
intervene in the electronic meeting includes: detecting, based on
performing sentiment analysis on the audio/video data, an ongoing
discussion to be interrupted.
4. The apparatus of claim 1, wherein the cue comprises a natural
language request for the apparatus to retrieve stored information
related to a different electronic meeting.
5. The apparatus of claim 1, wherein processing of the intervention
data comprising the second meeting content data at the one or more
nodes provides a visual indication of a different agenda topic.
6. The apparatus of claim 1, wherein processing of the intervention
data comprising the second meeting content data at the one or more
nodes provides an indication that another electronic meeting has
been automatically scheduled for continuing an interrupted
discussion.
7. The apparatus of claim 1, wherein processing of the intervention
data comprising the second meeting content data at the one or more
nodes provides machine-lettering or a machine-drawn image that has
been converted from handwritten notes or a hand-drawn illustration
received as input from an electronic whiteboard.
8. One or more non-transitory computer-readable media storing
instructions which, when processed by one or more processors,
cause: receiving, at an apparatus, audio/video data comprising
first meeting content data for an electronic meeting that includes
a plurality of participants; determining, by the apparatus, that
the audio/video data comprising first meeting content data for an
electronic meeting that includes a plurality of participants
comprises a cue for the apparatus to intervene in the electronic
meeting, wherein determining, by the apparatus, that the
audio/video data comprises a cue for the apparatus to intervene in
the electronic meeting includes determining, by the apparatus, that
the audio/video data is related to a particular agenda topic for
the electronic meeting; in response to the cue to intervene in the
electronic meeting, generating, by the apparatus, intervention data
comprising second meeting content data that is different from the
first meeting content data and indicates that the audio/video data
is related to the particular agenda topic for the electronic
meeting; during the electronic meeting, sending the intervention
data comprising second meeting content data that is different from
the first meeting content data and indicates that the audio/video
data is related to the particular agenda topic for the electronic
meeting, from the apparatus to one or more nodes associated with at
least one participant of the plurality of participants, wherein
processing of the second meeting content data at the one or more
nodes associated with at least one participant of the plurality of
participants provides a visual indication of the particular agenda
topic.
9. The one or more non-transitory computer-readable media of claim
8, wherein determining, by the apparatus, that the audio/video data
is related to the particular agenda topic for the electronic
meeting includes performing speech or text recognition on the
audio/video data.
10. The one or more non-transitory computer-readable media of claim
8, wherein determining, by the apparatus, that the audio/video data
comprises a cue for the apparatus to intervene in the electronic
meeting includes: detecting, based on performing sentiment analysis
on the audio/video data, an ongoing discussion to be
interrupted.
11. The one or more non-transitory computer-readable media of claim
8, wherein the cue comprises a natural language request for the
apparatus to retrieve stored information related to a different
electronic meeting.
12. The one or more non-transitory computer-readable media of claim
8, wherein processing of the intervention data comprising the
second meeting content data at the one or more nodes provides a
visual indication of a different agenda topic.
13. The one or more non-transitory computer-readable media of claim
8, wherein processing of the intervention data comprising the
second meeting content data at the one or more nodes provides an
indication that another electronic meeting has been automatically
scheduled for continuing an interrupted discussion.
14. The one or more non-transitory computer-readable media of claim
8, wherein processing of the intervention data comprising the
second meeting content data at the one or more nodes provides
machine-lettering or a machine-drawn image that has been converted
from handwritten notes or a hand-drawn illustration received as
input from an electronic whiteboard.
15. A method comprising: receiving, at an apparatus, audio/video
data comprising first meeting content data for an electronic
meeting that includes a plurality of participants; determining, by
the apparatus, that the audio/video data comprising first meeting
content data for an electronic meeting that includes a plurality of
participants comprises a cue for the apparatus to intervene in the
electronic meeting, wherein determining, by the apparatus, that the
audio/video data comprises a cue for the apparatus to intervene in
the electronic meeting includes determining, by the apparatus, that
the audio/video data is related to a particular agenda topic for
the electronic meeting; in response to the cue to intervene in the
electronic meeting, generating, by the apparatus, intervention data
comprising second meeting content data that is different from the
first meeting content data and indicates that the audio/video data
is related to the particular agenda topic for the electronic
meeting; during the electronic meeting, sending the intervention
data comprising second meeting content data that is different from
the first meeting content data and indicates that the audio/video
data is related to the particular agenda topic for the electronic
meeting, from the apparatus to one or more nodes associated with at
least one participant of the plurality of participants, wherein
processing of the second meeting content data at the one or more
nodes associated with at least one participant of the plurality of
participants provides a visual indication of the particular agenda
topic.
16. The method of claim 15, wherein determining, by the apparatus,
that the audio/video data is related to the particular agenda topic
for the electronic meeting includes performing speech or text
recognition on the audio/video data.
17. The method of claim 15, wherein determining, by the apparatus,
that the audio/video data comprises a cue for the apparatus to
intervene in the electronic meeting includes: detecting, based on
performing sentiment analysis on the audio/video data, an ongoing
discussion to be interrupted.
18. The method of claim 15, wherein the cue comprises a natural
language request for the apparatus to retrieve stored information
related to a different electronic meeting.
19. The method of claim 15, wherein processing of the intervention
data comprising the second meeting content data at the one or more
nodes provides machine-lettering or a machine-drawn image that has
been converted from handwritten notes or a hand-drawn illustration
received as input from an electronic whiteboard.
20. The method of claim 15, wherein processing of the intervention
data comprising the second meeting content data at the one or more
nodes provides an indication that another electronic meeting has
been automatically scheduled for continuing an interrupted
discussion.
Description
PRIORITY CLAIM
[0001] This application claims benefit of Provisional Appln.
62/253,325, filed Nov. 10, 2015, the entire contents of which is
hereby incorporated by reference as if fully set forth herein,
under 35 U.S.C. .sctn.119(e).
FIELD OF THE DISCLOSURE
[0002] Embodiments relate to artificial intelligence and more
specifically, to electronic meeting intelligence.
BACKGROUND
[0003] The approaches described in this section are approaches that
could be pursued, but not necessarily approaches that have been
previously conceived or pursued. Therefore, unless otherwise
indicated, it should not be assumed that any of the approaches
described in this section qualify as prior art merely by virtue of
their inclusion in this section.
[0004] A meeting is typically an effective vehicle for coordinating
the successful accomplishment of a common goal shared by multiple
people. However, a meeting can also devolve into a
counterproductive use of time in the absence of proper organization
of the meeting itself. For example, too much time may be devoted to
a particular topic that involves a small subset of meeting
attendees, and this may result in wasted time for the remaining
attendees. Such circumstances may be avoided through the use of a
person serving as a meeting moderator, but personal biases may
affect the neutrality of the person serving as the meeting
moderator. Such circumstances may also be avoided through adequate
preparation for the meeting, but it may be impossible to foresee
all the possible issues that may arise during the meeting.
[0005] Another way for a meeting to result in wasted time is by
failing to fully reap the benefits provided by the meeting. For
example, transcribing the meeting, scheduling an additional
meeting, analyzing meeting participation, and/or researching an
issue that was contended during the meeting may be tedious
follow-up actions that are neglected after the meeting. Even if the
follow-up actions are performed, the process of performing them may
be slow and cost-prohibitive.
[0006] Thus, it is desirable and beneficial to perform the
administrative duties related to a meeting using an approach
without the aforementioned shortcomings.
SUMMARY
[0007] An apparatus includes one or more processors and one or more
computer-readable media storing instructions which, when processed
by the one or more processors cause the apparatus to receive
audio/video data including first meeting content data for an
electronic meeting that includes multiple participants. The
instructions further cause the apparatus to determine that the
audio/video data includes a cue for the apparatus to intervene in
the electronic meeting. Further, the instructions cause the
apparatus to generate, in response to the cue, intervention data
including second meeting content data that is different from the
first meeting content data. Still further, the instructions cause
the apparatus to send, during the electronic meeting, the
intervention data to one or more nodes associated with at least one
participant of the multiple participants.
BRIEF DESCRIPTION OF THE DRAWINGS
[0008] In the drawings:
[0009] FIGS. 1A-C depict example computer architectures upon which
embodiments may be implemented.
[0010] FIG. 2 depicts an example participant interface.
[0011] FIG. 3 is a block diagram that depicts an arrangement for
generating intervention data.
[0012] FIGS. 4A-D depict examples of intervention data.
[0013] FIG. 5 is a block diagram that depicts an arrangement for
generating a report.
[0014] FIGS. 6A-C depict examples of meeting content metadata.
[0015] FIGS. 7A-B depict example reports.
[0016] FIG. 8 is a flow diagram that depicts an approach for
generating intervention data.
[0017] FIG. 9 is a flow diagram that depicts an approach for
generating a report.
[0018] FIG. 10 depicts an example computer system upon which
embodiments may be implemented.
[0019] While each of the drawing figures depicts a particular
embodiment for purposes of depicting a clear example, other
embodiments may omit, add to, reorder, and/or modify any of the
elements shown in the drawing figures. For purposes of depicting
clear examples, one or more figures may be described with reference
to one or more other figures, but using the particular arrangement
depicted in the one or more other figures is not required in other
embodiments.
DETAILED DESCRIPTION
[0020] In the following description, for the purposes of
explanation, numerous specific details are set forth in order to
provide a thorough understanding of the present disclosure. It will
be apparent, however, that the present disclosure may be practiced
without these specific details. In other instances, well-known
structures and devices are shown in block diagram form in order to
avoid unnecessarily obscuring the present disclosure. Modifiers
such as "first" and "second" may be used to differentiate elements,
but the modifiers do not necessarily indicate any particular
order.
[0021] I. GENERAL OVERVIEW
[0022] II. NETWORK TOPOLOGY [0023] A. MEETING INTELLIGENCE
APPARATUS [0024] B. NETWORK INFRASTRUCTURE [0025] C. PARTICIPANT
NODES
[0026] III. REAL-TIME PROCESSING [0027] A. MEETING FLOW MANAGEMENT
[0028] B. INFORMATION RETRIEVAL SERVICES [0029] C. MEETING CONTENT
SUPPLEMENTATION [0030] D. MEETING CONTENT METADATA GENERATION
[0031] IV. POST-PROCESSING [0032] A. MEETING CONTENT ANALYSIS
[0033] B. MEETING SUMMARY [0034] C. PARTICIPANT ANALYSIS
[0035] V. PROCESS OVERVIEW [0036] A. GENERATING INTERVENTION DATA
[0037] B. GENERATING REPORTS
[0038] VI. IMPLEMENTATION MECHANISMS
I. General Overview
[0039] Artificial intelligence is introduced into an electronic
meeting context to perform various administrative tasks. The
administrative tasks include tasks performed during an electronic
meeting as well as after an electronic meeting. The artificial
intelligence performs the administrative tasks based on analyzing
meeting content using any of a number of input detection tools. For
example, the artificial intelligence can identify meeting
participants, provide translation services, respond to questions,
and serve as a meeting moderator. The artificial intelligence can
also include elements of its meeting content analysis in various
reports. For example, the reports can include meeting transcripts,
follow-up items, meeting efficiency metrics, and meeting
participant analyses.
II. Network Topology
[0040] FIGS. 1A-C depict example computer architectures upon which
embodiments may be implemented. FIGS. 1A-C include various
arrangements of electronic meeting 100. Electronic meeting 100
includes meeting intelligence apparatus 102 and one or more nodes
106A-N communicatively coupled via network infrastructure 104.
Nodes 106A-N are associated with a plurality of participants
108A-N.
[0041] Electronic meeting 100 may be an audioconferencing session,
a videoconferencing session, and/or any other meeting involving
data transmissions between network infrastructure 104 and at least
one node 106A. Referring to FIGS. 1A-B, electronic meeting 100
includes a virtual gathering of participants 108A-N. In the
examples of FIGS. 1A-B, participants 108A-N may be located in
different physical locations yet communicate with each other via
network infrastructure 104. Referring to FIG. 1C, electronic
meeting 100 includes a physical gathering of participants 108A-N.
In the example of FIG. 1C, participants 108A-N may be located in
physical proximity to each other such that they may communicate
with each other without network infrastructure 104. However,
network infrastructure 104 may enable participants 108A-N to
interact with meeting intelligence apparatus 102, which receives
input data from and/or sends output data to node 106A.
[0042] In an embodiment, electronic meeting 100 involves a network
of computers. A "computer" may be one or more physical computers,
virtual computers, and/or computing devices. A computer may be a
client and/or a server. Any reference to "a computer" herein may
mean one or more computers, unless expressly stated otherwise. Each
of the logical and/or functional units depicted in any of the
figures or described herein may be implemented using any of the
techniques further described herein in connection with FIG. 10.
[0043] A. Meeting Intelligence Apparatus
[0044] In an embodiment, meeting intelligence apparatus 102 is a
computer endowed with artificial intelligence. The computer may be
a special-purpose computer dedicated to providing artificial
intelligence to electronic meetings or a generic computer executing
one or more services that provide artificial intelligence to
electronic meetings. In other words, meeting intelligence may be
implemented using hardware, software, and/or firmware. Non-limiting
examples include Ricoh Brain and IBM Watson. Meeting intelligence
apparatus 102 may always be available (e.g., involve continuously
running processes) or may be available on demand (e.g., be powered
on when needed). For example, meeting intelligence apparatus 102
may be replicated over multiple computers such that at any point in
time, at least one computer can provide meeting intelligence
services.
[0045] Meeting intelligence apparatus 102 can access meeting
content data as if it were a node associated with a participant in
electronic meeting 100. Thus, meeting intelligence apparatus 102
may access any meeting content data that is transmitted from any of
the one or more nodes 106A-N involved in electronic meeting 100.
For example, meeting intelligence apparatus 102 may monitor,
collect, and/or analyze all data transmissions during electronic
meeting 100.
[0046] Meeting intelligence apparatus 102 can analyze meeting
content data using any of a number of tools, such as speech or text
recognition, voice or face identification, sentiment analysis,
object detection, gestural analysis, thermal imaging, etc. Based on
analyzing the meeting content data, meeting intelligence apparatus
102 performs any of a number of automated tasks, such as providing
a translation, responding to an information request, moderating
electronic meeting 100, generating a report, etc.
[0047] Meeting intelligence apparatus 102 may be located at a
number of different locations relative to network infrastructure
104. Referring to FIGS. 1A and 1C, meeting intelligence apparatus
102 is located outside network infrastructure 104. Referring to
FIG. 1B, meeting intelligence apparatus 102 is collocated with at
least some of network infrastructure 104.
[0048] In an embodiment, meeting intelligence apparatus 102 is
communicatively coupled to a meeting repository (not shown). The
meeting repository may be part of meeting intelligence apparatus
102 or may be located on a separate device from meeting
intelligence apparatus 102. The meeting repository may be a
database, a configuration file, and/or any other system or data
structure that stores meeting data related to one or more
electronic meetings. For example, meeting intelligence apparatus
102 may collect and store, in the meeting repository, meeting
content data related to multiple meetings. In other words, meeting
intelligence apparatus 102 can provide the services of a librarian
for meeting-related data.
[0049] Like meeting intelligence apparatus 102, the meeting
repository may be located at a number of different locations
relative to network infrastructure 104. For example, the meeting
repository may be a data structure stored in memory on one or more
computers of network infrastructure 104.
[0050] In an embodiment, meeting intelligence apparatus 102 is
communicatively coupled to any of a number of external data sources
(not shown), such as websites or databases managed by Salesforce,
Oracle, SAP, Workday, or any entity other than the entity managing
meeting intelligence apparatus 102. Meeting intelligence apparatus
102 may be communicatively coupled to the external data sources via
network infrastructure 104. The external data sources may provide
meeting intelligence apparatus 102 with access to any of a variety
of data, meeting-related or otherwise.
[0051] B. Network Infrastructure
[0052] Network infrastructure 104 may include any number and type
of wired or wireless networks, such as local area networks (LANs),
wide area networks (WANs), the Internet, etc. Network
infrastructure 104 may also include one or more computers, such as
one or more server computers, load-balancing computers, cloud-based
computers, data centers, storage devices, and/or any other
special-purpose computing devices. For example, network
infrastructure 104 may include a Unified Communication System (UCS)
Service Platform by Ricoh Company Ltd., and/or any other
computer(s) that manage(s) electronic meeting 100.
[0053] C. Participant Nodes
[0054] Each node of the one or more nodes 106A-N is associated with
one or more participants 108A-N. Each participant is a person who
participates in electronic meeting 100. Each node processes data
transmission between network infrastructure 104 and at least one
participant. Multiple nodes 106A-N may be communicatively coupled
with each other using any of a number of different configurations.
For example, multiple nodes may be communicatively coupled with
each other via a centralized server or via a peer-to-peer
network.
[0055] In an embodiment, a node includes a computer that executes
an electronic meeting application. The node may include a
special-purpose computer, such as Ricoh UCS P3500, or a
general-purpose computer that executes a special-purpose
application, such as Ricoh UCS App. The node may also include any
of a number of input/output mechanisms, such as a camera, a
microphone, and an electronic whiteboard. For example, the node may
include a smartphone with GPS capability, a camera, a microphone,
an accelerometer, a touchscreen, etc.
[0056] The input/output mechanisms may include a participant
interface, such as a graphical user interface (GUI). FIG. 2 depicts
an example participant interface that is presented at node 106A.
Referring to FIG. 2, node 106A includes a web-based interface that
presents a variety of information to a participant during
electronic meeting 100. The web-based interface of FIG. 2 displays
video streams including participant identification data 206
associated with other participants, a meeting agenda managed by
meeting intelligence apparatus 102, and a message including
scheduling indication 204. The meeting agenda includes agenda topic
202 and visual indication 200 of a current agenda topic. As shall
be described in greater detail hereafter, meeting intelligence
apparatus 102 provides visual indication 200, scheduling indication
204, and/or participant identification data 206 based on analyzing
meeting content data.
III. Real-Time Processing
[0057] Meeting intelligence apparatus 102 can intervene during
electronic meeting 100 to provide any of a variety of intervention
data, such as visual indication 200, scheduling indication 204,
participant identification data 206, recommendation information,
and/or any other data that meeting intelligence apparatus 102
transmits during electronic meeting 100. FIG. 3 is a block diagram
that depicts an arrangement for generating intervention data.
Referring to FIG. 3, meeting intelligence apparatus 102 receives
audio/video data 300 from node 106A. Audio/video data 300 may be
one or more data packets, a data stream, and/or any other form of
data that includes audio and/or video information related to
electronic meeting 100. Audio/video data 300 includes first meeting
content data 302 which, in turn, includes cue 304. Meeting
intelligence apparatus 102 includes cue detection logic 306, which
determines whether audio/video data 300 includes cue 304. Meeting
intelligence apparatus 102 also includes data generation logic 308,
which generates intervention data 310 if audio/video data 300
includes cue 304. Meeting intelligence apparatus 102 sends
intervention data 310 to node 106A during electronic meeting 100.
Intervention data 310 includes second meeting content data 312.
[0058] Meeting intelligence apparatus 102 can intervene in
electronic meeting 100 in any of a number of ways. Non-limiting
examples include intervening to manage meeting flow, to provide
information retrieval services, and/or to supplement meeting
content.
[0059] A. Meeting Flow Management
[0060] FIGS. 4A-B are block diagrams that depict arrangements for
managing meeting flow. Meeting intelligence apparatus 102 can
manage meeting flow in any of a number of ways. For example,
meeting intelligence apparatus 102 can ensure that electronic
meeting 100 follows a predetermined meeting schedule, such as a
flowchart or a meeting agenda with a respective time limit for each
agenda topic 202. Additionally or alternatively, meeting
intelligence apparatus 102 can defuse a heated situation before it
affects the progress of electronic meeting 100.
[0061] FIG. 4A is a block diagram that depicts an arrangement for
performing speech or text recognition to determine that audio/video
data 300 is related to a particular agenda topic. Referring to FIG.
4A, first meeting content data 302 includes the speech or text
statement "Gross sales are expected to be $10.8 million next
quarter." For example, a participant associated with node 106A may
have caused first meeting content data 302 to be generated by
speaking, writing, typing, or displaying the statement. Meeting
intelligence apparatus 102 includes speech or text recognition
logic 400, which parses first meeting content data 302 and detects
at least the keywords "next quarter". The keywords are a cue 304
for meeting intelligence apparatus 102 to generate intervention
data 310 that indicates the appropriate agenda topic. For example,
intervention data 310 may cause a continued indication of the
current agenda topic or cause an indication of a different agenda
topic. In the example of FIG. 4A, second meeting content data 312
specifies, among other information, the position of visual
indication 200 using JavaScript Object Notation (JSON). Thus, one
or more nodes 106A-N that process the JSON will display visual
indication 200 at the specified position in the meeting agenda
during electronic meeting 100.
[0062] FIG. 4B is a block diagram that depicts an arrangement for
performing sentiment analysis to detect an ongoing discussion 402
to be interrupted. Referring to FIG. 4B, meeting intelligence
apparatus 102 includes sentiment analysis logic 404, which performs
sentiment analysis on first meeting content data 302 related to
ongoing discussion 402. For example, meeting intelligence apparatus
102 may detect an angry tone or sentiment that is a cue 304 for
meeting intelligence apparatus 102 to generate intervention data
310 indicating that another electronic meeting has been
automatically scheduled for continuing ongoing discussion 402. In
the example of FIG. 4B, second meeting content data 312 includes
JSON from which scheduling indication 204 can be generated during
electronic meeting 100.
[0063] Meeting intelligence apparatus 102 may use a timer or
counter in conjunction with any combination of elements from the
foregoing examples. For example, after meeting intelligence
apparatus 102 detects a discussion of a particular agenda topic,
meeting intelligence apparatus 102 may compare a timer value to a
predetermined time limit for the particular agenda topic. If the
timer value exceeds the predetermined time limit, meeting
intelligence apparatus 102 may cause scheduling indication 204 to
be generated. Additionally or alternatively, meeting intelligence
apparatus 102 may cause visual indication 200 of a different agenda
topic.
[0064] B. Information Retrieval Services
[0065] Meeting intelligence apparatus 102 can provide information
retrieval services in a user-friendly manner. Significantly, a
participant of electronic meeting 100 may formulate an information
request in a natural language instead of a computer language, such
as Structured Query Language (SQL).
[0066] FIG. 4C is a block diagram that depicts an arrangement for
retrieving requested information. Referring to FIG. 4C, meeting
intelligence apparatus 102 receives natural language request 406,
which includes the question "Where did we leave off at the last
meeting?" Note that natural language request 406 may include a
question, a statement, a command, or any other type of request for
information. Speech or text recognition logic 400 parses and
interprets first meeting content data 302 to detect natural
language request 406, which is a cue 304 for meeting intelligence
apparatus 102 to generate intervention data 310 to be sent to at
least node 106A during electronic meeting 100. For example, speech
or text recognition logic 400, alone or in combination with
sentiment analysis logic 404, may detect inflected speech and/or
keywords indicative of an information request, such as "who",
"what", "when", "where", "why", or "how". Meeting intelligence
apparatus 102 can interpret these and other keywords as commands to
perform requested functions, such as data retrieval.
[0067] In the example of FIG. 4C, meeting intelligence apparatus
102 may interpret the question as a command to search and analyze
prior meeting data to determine an answer to the question.
Determining the answer to the question may involve analyzing
meeting content data related to an ongoing meeting and/or a prior
meeting, thereby increasing the relevancy of the answer to the
question. For example, the question "Where did we leave off at the
last meeting?" may be analyzed using contextual data (e.g.,
metadata) from the current meeting, such as the identities of
participants 108A-N, the topic of the current discussion, etc.
Meeting intelligence apparatus 102 may search the meeting
repository for information that most closely matches the contextual
data from the current meeting. For example, meeting intelligence
apparatus 102 may search the meeting repository for any prior
meetings that included some or all of the participants 108A-N of
the current meeting and rank the results. Meeting intelligence
apparatus 102 may then determine that the "last meeting" refers to
the top result and may search for the last agenda topic in the
prior meeting that corresponds to the top result.
[0068] Intervention data 310 that is generated in response to
natural language request 406 includes stored information 410 that
meeting intelligence apparatus 102 retrieves in response to natural
language request 406. Meeting intelligence apparatus 102 includes
data retrieval logic 408, which performs a search for stored
information 410 that is responsive to natural language request 406.
For example, data retrieval logic 408 may search a meeting
repository and/or external data sources, such as websites on the
Internet. In the example of FIG. 4C, meeting intelligence apparatus
102 generates second meeting content data 312 that includes stored
information 410 retrieved from a meeting repository. The stored
information 410 includes the answer to the question about a
different meeting.
[0069] In an embodiment, meeting intelligence apparatus 102 may
process natural language request 406 and research a particular
topic or otherwise search for information that is unrelated to a
particular meeting. For example, natural language request 406 may
be the statement "We need to figure out how to get source code from
the app." In response, meeting intelligence apparatus 102 may
retrieve information from various websites that address natural
language request 406. As shall be described in greater detail
hereafter, this can be a particularly useful feature for
participants 108A-N who wish to collaborate, during electronic
meeting 100, to create a presentation, a report, or any other
document.
[0070] C. Meeting Content Supplementation
[0071] Meeting intelligence apparatus 102 can supplement first
meeting content data 302 with second meeting content data 312 in
any of a number of ways. For example, meeting intelligence
apparatus 102 may cause participant identifiers to be presented at
one or more nodes 106A-N. Additionally or alternatively, meeting
intelligence apparatus 102 may cause a language translation or
format conversion of first meeting content data 302 to be presented
at one or more nodes 106A-N.
[0072] FIG. 4D is a block diagram that depicts an arrangement for
supplementing meeting content with participant identification data.
Referring to FIG. 4D, meeting intelligence apparatus 102 includes
voice or face recognition logic 412, which performs voice or face
recognition on first meeting content data 302 to detect a voice or
a face. The voice or face is a cue 304 for meeting intelligence
apparatus 102 to generate intervention data 310 to be sent to at
least node 106A during electronic meeting 100. In response to
detecting the cue 304, meeting intelligence apparatus 102
determines one or more participants 108A-N and generates
participant identification data 206 that identifies the one or more
participants 108A-N. Meeting intelligence apparatus 102 generates
and transmits second meeting content data 312 that includes
participant identification data 206. When processed at one or more
nodes 106A-N, second meeting content data 312 causes participant
identification data 206 to be presented at the one or more nodes
106A-N.
[0073] In an embodiment, meeting intelligence apparatus 102 can
perform speech or text recognition on first meeting content data
302 to detect a particular language, which may be a cue 304 for
meeting intelligence apparatus 102 to generate second meeting
content data 312 that includes a translation of first meeting
content data 302 into a different language. For example, meeting
intelligence apparatus 102 may translate English content into
Japanese content. Second meeting content data 312 may replace or
supplement first meeting content data 302. For example, second
meeting content data 312 may cause Japanese dubbing of first
meeting content data 302 or may cause Japanese subtitles to be
added to first meeting content 302.
[0074] In an embodiment, meeting intelligence apparatus 102 can
detect input from an input/output mechanism, and the input may be a
cue 304 for meeting intelligence apparatus 102 to convert the input
into a different format. For example, the input/output mechanism
may be an electronic whiteboard that receives as input first
meeting content data 302 in the form of handwritten notes or
hand-drawn illustrations. Based on optical character recognition
(OCR), vector graphics, and/or any other data conversion tool,
meeting intelligence apparatus 102 may convert first meeting
content data 302 into second meeting content data 312 in the form
of machine-lettering or a machine-drawn image. When processed at
one or more nodes 106A-N, second meeting content data 312 may cause
the machine-lettering or the machine-drawn image to be provided as
output on the electronic whiteboard.
[0075] D. Meeting Content Metadata Generation
[0076] FIGS. 4A-D each depict second meeting content data 312 that
includes a variety of meeting content metadata. Meeting
intelligence apparatus 102 generates the meeting content metadata
based on internal and/or external information. Internal information
includes information readily accessible to meeting intelligence
apparatus 102 even in the absence of a network connection. For
example, if meeting intelligence apparatus 102 is a computer, the
system date and time are internal information. In contrast,
external information includes information accessible to meeting
intelligence apparatus 102 via a network connection. For example,
information retrieved from external data sources are external
information.
[0077] FIGS. 4A-D each depict sending meeting content metadata to
one or more nodes 106A-N during electronic meeting 100. However,
some meeting content metadata may remain untransmitted throughout
the duration of electronic meeting 100. For example, some meeting
content metadata may remain stored in meeting intelligence
apparatus 102 for an internal use, such as generating a report. As
shall be described in greater detail in FIG. 6C, a notable example
of such meeting content metadata is a label that identifies a key
meeting point, such as an action item, a task, a deadline, etc.
IV. Post-Processing
[0078] Meeting intelligence apparatus 102 can provide any of a
number of services outside of electronic meeting 100 based on
analyzing meeting content. Meeting intelligence apparatus 102 may
analyze meeting content at any time relative to electronic meeting
100. For example, after electronic meeting 100 ends, meeting
intelligence apparatus 102 may analyze stored meeting content data
and generate a report based on analyzed meeting content data.
Alternatively, meeting intelligence apparatus 102 may analyze
meeting content data during electronic meeting 100 and may
generate, after electronic meeting 100 ends, a report based on
analyzed meeting content data. The report may be any of a number of
documents, such as a meeting agenda, a meeting summary, a meeting
transcript, a meeting participant analysis, a slideshow
presentation, etc.
[0079] FIG. 5 is a block diagram that depicts an arrangement for
generating a report. Referring to FIG. 5, meeting intelligence
apparatus 102 receives, from node 106A, audio/video data 300 that
includes first meeting content data 302. Meeting intelligence
apparatus 102 includes data extraction logic 500, metadata
generation logic 502, and report generation logic 506. Data
extraction logic 500 causes first meeting content data 302 to be
extracted from audio/video data 300. Meeting intelligence apparatus
102 analyzes first meeting content data 302 and uses metadata
generation logic 502 to generate meeting content metadata 504.
Report generation logic 506 causes meeting content metadata 504 to
be included in report 508.
[0080] Meeting intelligence apparatus 102 may do any of a number of
things with report 508. For example, meeting intelligence apparatus
102 may store report 508 in a meeting repository or provide report
508 to one or more nodes 106A-N associated with participants 108A-N
of electronic meeting 100. Thus, meeting intelligence apparatus 102
may generate report 508 in an offline mode and/or an online
mode.
[0081] A. Meeting Content Analysis
[0082] In an embodiment, meeting intelligence apparatus 102
generates meeting content metadata 504 during electronic meeting
100. For example, data generation logic 308 may include metadata
generation logic 502, and second meeting content data 312 may
include meeting content metadata 504. FIGS. 6A-C depict examples of
meeting content metadata 504 that can be generated during
electronic meeting 100.
[0083] FIG. 6A is a block diagram that depicts an arrangement for
generating meeting content metadata 504 that includes participant
identification data 206. Referring to FIG. 6A, data extraction
logic 500 extracts and provides first meeting content data 302 to
metadata generation logic 502. In the example of FIG. 6A, metadata
generation logic 502 includes voice or face recognition logic 412,
which performs voice or face recognition on first meeting content
data 302 to identify one or more participants 108A-N in electronic
meeting 100. Metadata generation logic 502 generates meeting
content metadata 504 that includes participant identification data
206 for the one or more participants 108A-N. Metadata generation
logic 502 provides meeting content metadata 504 to report
generation logic 506.
[0084] FIG. 6B is a block diagram that depicts an arrangement for
generating meeting content metadata 504 that includes a sentiment
detected in first meeting content data 302. Referring to FIG. 6B,
data extraction logic 500 extracts first meeting content data 302
that includes the statement "Not necessarily." Metadata generation
logic 502 includes sentiment analysis logic 404, which performs
sentiment analysis on first meeting content data 302 to determine
sentiment 600 of a participant in electronic meeting 100. Meeting
generation logic 502 generates meeting content metadata 504 that
includes sentiment 600. In the example of FIG. 6B, meeting content
metadata 504 also includes participant identification data 206 and
information related to providing a translation of first meeting
content data 302. Thus, metadata generation logic 502 can include a
combination of sentiment analysis logic 404, voice or face
recognition logic 412, and speech or text recognition logic
400.
[0085] FIG. 6C is a block diagram that depicts an arrangement for
generating meeting content metadata 504 that includes a label to
identify a key meeting point. Referring to FIG. 6C, first meeting
content data 302 includes the statement "Action item create
schedule by Tuesday". Metadata generation logic 502 includes speech
or text recognition logic 400, which performs speech or text
recognition on first meeting content data 302 to recognize one or
more keywords 602 in first meeting content data 302. The one or
more keywords 602 may indicate a task 604 to be completed after
electronic meeting 100. For example, the one or more keywords 602
may include a voice or text command to perform a particular task.
In the example of FIG. 6C, the one or more keywords 602 are the
label "Action item" followed by the command "create schedule by
Tuesday". Metadata generation logic 502 generates meeting content
metadata 504 that includes the one or more keywords 602 and/or the
task 604.
[0086] Meeting intelligence apparatus 102 may generate meeting
content metadata 504 based on internal and/or external information,
such as geolocation information or a meeting room availability
schedule. In each of FIGS. 6A-C, report generation logic 506
includes meeting content metadata 504 in report 508. FIGS. 7A-B
depict examples of report 508. Referring to FIGS. 7A-B, meeting
intelligence apparatus 102 provides report 508 via a web-based
participant interface. Meeting intelligence apparatus 102 may send
report 508 to one or more nodes 106A-N at any of a number of times,
such as upon demand, upon detecting a network connection,
automatically after each electronic meeting 100, etc.
[0087] B. Meeting Summary
[0088] FIG. 7A depicts an example meeting summary. In the example
of FIG. 7A, report 508 is a meeting summary that includes many of
the meeting content metadata 504 depicted in FIGS. 6A-C. A meeting
summary may include explicit data and/or implicit data. Explicit
data includes meeting content data, such as documents, images,
and/or any other data originating from the one or more nodes
106A-N. In the example of FIG. 7A, explicit data may include the
meeting agenda, the list of "Action Items", and/or the list of
"Documents". Implicit data includes meeting content metadata 504,
such as identifiers, translations, and/or any other data generated
by meeting intelligence apparatus 102. For example, the meeting
summary may include a drop-down list providing links to a meeting
transcript 700 in the multiple languages depicted in FIG. 6B. As
another example, the participant identification data 206 depicted
in FIG. 6A are provided in the meeting summary as links to
individual reports related to each participant. As shall be
described in greater detail in FIG. 7B, the individual reports may
include participant metrics.
[0089] In the example of FIG. 7A, the meeting summary also includes
underlined links to other reports, such as the meeting agenda
depicted in FIG. 2, task 604 depicted in FIG. 6C, and various
documents generated based on one or more input/output mechanisms.
For example, the one or more input/output mechanisms may include an
electronic whiteboard. Meeting intelligence apparatus 102 may
convert any handwritten notes or hand-drawn illustrations received
as input on the electronic whiteboard into a machine-lettered or a
machine-drawn image based on optical character recognition (OCR),
vector graphics, and/or any other data conversion tool. For
example, meeting intelligence apparatus 102 may perform OCR on
handwritten notes to generate metadata that indicates which letters
were detected. The metadata may then be used to generate the
detected letters in a particular font or any other
machine-lettering format.
[0090] In an embodiment, the meeting summary may include graphical
depictions of meeting efficiency. In the example of FIG. 7A, the
meeting summary includes a pie chart that details the amount of
time spent during electronic meeting 100 on each agenda topic 202.
FIG. 7A also includes a bar representing an efficiency spectrum. An
arrow and/or a colored portion of the bar may indicate the relative
position on the bar for a particular meeting.
[0091] C. Participant Analysis
[0092] FIG. 7B depicts an example participant analysis. As
described above, selecting a particular participant in the meeting
summary may cause an individual report for the selected participant
to be presented. The individual report may include participation
metrics 702 for the selected participant. In the example of FIG.
7B, report 508 is the individual report "Meeting Participant
Profile." Among the participation metrics 702 depicted in FIG. 7B
are the amount of participation time for the selected participant,
a participation index for the selected participant, a role
associated with the selected participant, and a list of timestamped
sentiments detected for the selected participant. The participation
index may be any measure, weighted or otherwise, of any aspect of
the selected participant's contribution to the meeting. For
example, "63/100" may indicate a proportion of the total meeting
time during which the selected participant spoke. The role
associated with the selected participant may indicate any of a
number of categories that describe the selected participant
relative to the current meeting and/or within a particular entity
(e.g., a vice-president of a corporation). For example, "Active
Presenter" may indicate that the selected participant did not
merely respond to other participants, but also provided many of the
topics for discussion.
V. Process Overview
[0093] FIGS. 8 and 9 are flow diagrams that depict various
processes that can be performed by meeting intelligence apparatus
102. In an embodiment, FIG. 8 depicts a process that is performed
with a network connection during electronic meeting 100. In an
embodiment, FIG. 9 depicts a process that can be performed, at
least partially, with or without a network connection.
[0094] A. Generating Intervention Data
[0095] FIG. 8 is a flow diagram that depicts an approach for
generating intervention data 310. At block 800, a meeting
intelligence apparatus 102 receives audio/video data 300 for an
electronic meeting 100 that includes a plurality of participants
108A-N. The audio/video data 300 includes first meeting content
data 302 for the electronic meeting 100. For example, Ricoh Brain
may receive a videoconference stream from a Ricoh UCS P3500
associated with Alice, who is making an offer to Bob during the
electronic meeting 100.
[0096] At block 802, the meeting intelligence apparatus 102
determines that the audio/video data 300 includes a cue 304 for the
meeting intelligence apparatus 102 to intervene in the electronic
meeting 100. The meeting intelligence apparatus 102 may make this
determination based on performing any of a number of analyses on
the audio/video data 300, such as speech or text recognition, voice
or face recognition, sentiment analysis, etc. For example, Ricoh
Brain may extract and analyze first meeting content data 302 to
detect poor eye contact by Alice. The poor eye contact may be a cue
304 for Ricoh Brain to respond by sending a recommendation to
Bob.
[0097] At block 804, the meeting intelligence apparatus 102
generates intervention data 310 in response to detecting the cue
304. The intervention data 310 includes second meeting content data
312 that is different from the first meeting content data 302. For
example, Ricoh Brain may generate a recommendation that advises Bob
to make a counteroffer.
[0098] At block 806, the meeting intelligence apparatus 102 sends
the intervention data 310 to one or more nodes 106A-N during the
electronic meeting 100. The one or more nodes 106A-N are associated
with at least one participant of the plurality of participants
108A-N. For example, Ricoh Brain may send the recommendation to Bob
and withhold the recommendation from Alice.
[0099] B. Generating Reports
[0100] FIG. 9 is a flow diagram that depicts an approach for
generating a report 508. At block 900, a meeting intelligence
apparatus 102 receives audio/video data 300 for an electronic
meeting 100 that includes a plurality of participants 108A-N. For
example, Ricoh Brain may receive an audioconference data packet
from Charlie's smartphone, which is executing the Ricoh UCS
app.
[0101] At block 902, the meeting intelligence apparatus 102
extracts meeting content data from the audio/video data 300. For
example, Ricoh Brain may strip out header data and analyze the
payload of the audioconference data packet. Analyzing the payload
may involve performing speech or text recognition, sentiment
analysis, voice or face recognition, etc.
[0102] At block 904, the meeting intelligence apparatus 102
generates meeting content metadata 504 based on analyzing the
meeting content data. For example, Ricoh Brain may perform voice
recognition on the meeting content data to identify Charlie as the
person presenting at the electronic meeting 100. Ricoh Brain may
generate JSON that includes "speaker: Charlie" among the name-value
pairs.
[0103] At block 906, the meeting intelligence apparatus 102
includes at least part of the meeting content metadata 504 in a
report 508 of the electronic meeting 100. For example, Ricoh Brain
may generate a "Meeting Summary" report that includes "Charlie"
among the participants 108A-N of the electronic meeting 100.
VI. Implementation Mechanisms
[0104] According to one embodiment, the techniques described herein
are implemented by one or more special-purpose computing devices.
The special-purpose computing devices may be hard-wired to perform
the techniques, or may include digital electronic devices such as
one or more application-specific integrated circuits (ASICs) or
field programmable gate arrays (FPGAs) that are persistently
programmed to perform the techniques, or may include one or more
general purpose hardware processors programmed to perform the
techniques pursuant to program instructions in firmware, memory,
other storage, or a combination. Such special-purpose computing
devices may also combine custom hard-wired logic, ASICs, or FPGAs
with custom programming to accomplish the techniques. The
special-purpose computing devices may be desktop computer systems,
portable computer systems, handheld devices, networking devices or
any other device that incorporates hard-wired and/or program logic
to implement the techniques.
[0105] For example, FIG. 10 is a block diagram that depicts a
computer system 1000 upon which an embodiment may be implemented.
Computer system 1000 includes a bus 1002 or other communication
mechanism for communicating information, and a hardware processor
1004 coupled with bus 1002 for processing information. Hardware
processor 1004 may be, for example, a general purpose
microprocessor.
[0106] Computer system 1000 also includes a main memory 1006, such
as a random access memory (RAM) or other dynamic storage device,
coupled to bus 1002 for storing information and instructions to be
executed by processor 1004. Main memory 1006 also may be used for
storing temporary variables or other intermediate information
during execution of instructions to be executed by processor 1004.
Such instructions, when stored in non-transitory storage media
accessible to processor 1004, render computer system 1000 into a
special-purpose machine that is customized to perform the
operations specified in the instructions.
[0107] Computer system 1000 further includes a read only memory
(ROM) 1008 or other static storage device coupled to bus 1002 for
storing static information and instructions for processor 1004. A
storage device 1010, such as a magnetic disk or optical disk, is
provided and coupled to bus 1002 for storing information and
instructions.
[0108] Computer system 1000 may be coupled via bus 1002 to a
display 1012, such as a cathode ray tube (CRT), for displaying
information to a computer user. An input device 1014, including
alphanumeric and other keys, is coupled to bus 1002 for
communicating information and command selections to processor 1004.
Another type of user input device is cursor control 1016, such as a
mouse, a trackball, or cursor direction keys for communicating
direction information and command selections to processor 1004 and
for controlling cursor movement on display 1012. This input device
typically has two degrees of freedom in two axes, a first axis
(e.g., x) and a second axis (e.g., y), that allows the device to
specify positions in a plane.
[0109] Computer system 1000 may implement the techniques described
herein using customized hard-wired logic, one or more ASICs or
FPGAs, firmware and/or program logic which in combination with the
computer system causes or programs computer system 1000 to be a
special-purpose machine. According to one embodiment, the
techniques herein are performed by computer system 1000 in response
to processor 1004 executing one or more sequences of one or more
instructions contained in main memory 1006. Such instructions may
be read into main memory 1006 from another storage medium, such as
storage device 1010. Execution of the sequences of instructions
contained in main memory 1006 causes processor 1004 to perform the
process steps described herein. In alternative embodiments,
hard-wired circuitry may be used in place of or in combination with
software instructions.
[0110] The term "storage media" as used herein refers to any
non-transitory media that store data and/or instructions that cause
a machine to operation in a specific fashion. Such storage media
may comprise non-volatile media and/or volatile media. Non-volatile
media includes, for example, optical or magnetic disks, such as
storage device 1010. Volatile media includes dynamic memory, such
as main memory 1006. Common forms of storage media include, for
example, a floppy disk, a flexible disk, hard disk, solid state
drive, magnetic tape, or any other magnetic data storage medium, a
CD-ROM, any other optical data storage medium, any physical medium
with patterns of holes, a RAM, a PROM, and EPROM, a FLASH-EPROM,
NVRAM, any other memory chip or cartridge.
[0111] Storage media is distinct from but may be used in
conjunction with transmission media. Transmission media
participates in transferring information between storage media. For
example, transmission media includes coaxial cables, copper wire
and fiber optics, including the wires that comprise bus 1002.
Transmission media can also take the form of acoustic or light
waves, such as those generated during radio-wave and infra-red data
communications.
[0112] Various forms of media may be involved in carrying one or
more sequences of one or more instructions to processor 1004 for
execution. For example, the instructions may initially be carried
on a magnetic disk or solid state drive of a remote computer. The
remote computer can load the instructions into its dynamic memory
and send the instructions over a telephone line using a modem. A
modem local to computer system 1000 can receive the data on the
telephone line and use an infra-red transmitter to convert the data
to an infra-red signal. An infra-red detector can receive the data
carried in the infra-red signal and appropriate circuitry can place
the data on bus 1002. Bus 1002 carries the data to main memory
1006, from which processor 1004 retrieves and executes the
instructions. The instructions received by main memory 1006 may
optionally be stored on storage device 1010 either before or after
execution by processor 1004.
[0113] Computer system 1000 also includes a communication interface
1018 coupled to bus 1002. Communication interface 1018 provides a
two-way data communication coupling to a network link 1020 that is
connected to a local network 1022. For example, communication
interface 1018 may be an integrated services digital network (ISDN)
card, cable modem, satellite modem, or a modem to provide a data
communication connection to a corresponding type of telephone line.
As another example, communication interface 1018 may be a local
area network (LAN) card to provide a data communication connection
to a compatible LAN. Wireless links may also be implemented. In any
such implementation, communication interface 1018 sends and
receives electrical, electromagnetic or optical signals that carry
digital data streams representing various types of information.
[0114] Network link 1020 typically provides data communication
through one or more networks to other data devices. For example,
network link 1020 may provide a connection through local network
1022 to a host computer 1024 or to data equipment operated by an
Internet Service Provider (ISP) 1026. ISP 1026 in turn provides
data communication services through the world wide packet data
communication network now commonly referred to as the "Internet"
1028. Local network 1022 and Internet 1028 both use electrical,
electromagnetic or optical signals that carry digital data streams.
The signals through the various networks and the signals on network
link 1020 and through communication interface 1018, which carry the
digital data to and from computer system 1000, are example forms of
transmission media.
[0115] Computer system 1000 can send messages and receive data,
including program code, through the network(s), network link 1020
and communication interface 1018. In the Internet example, a server
1030 might transmit a requested code for an application program
through Internet 1028, ISP 1026, local network 1022 and
communication interface 1018.
[0116] The received code may be executed by processor 1004 as it is
received, and/or stored in storage device 1010, or other
non-volatile storage for later execution.
[0117] In the foregoing specification, embodiments have been
described with reference to numerous specific details that may vary
from implementation to implementation. The specification and
drawings are, accordingly, to be regarded in an illustrative rather
than a restrictive sense. The sole and exclusive indicator of the
scope of the disclosure, and what is intended by the applicants to
be the scope of the disclosure, is the literal and equivalent scope
of the set of claims that issue from this application, in the
specific form in which such claims issue, including any subsequent
correction.
* * * * *