U.S. patent application number 15/138518 was filed with the patent office on 2017-01-05 for crowd sourcing of device sensor data for real time response.
The applicant listed for this patent is International Business Machines Corporation. Invention is credited to Hernan A. Cunico, Jonathan Dunne, Jeremiah O'Connor, Asima Silva.
Application Number | 20170004213 15/138518 |
Document ID | / |
Family ID | 57684234 |
Filed Date | 2017-01-05 |
United States Patent
Application |
20170004213 |
Kind Code |
A1 |
Cunico; Hernan A. ; et
al. |
January 5, 2017 |
Crowd Sourcing of Device Sensor Data for Real Time Response
Abstract
A system, method, and computer-readable medium for performing a
crowdsourcing data analysis operation. More specifically, the
crowdsourcing data analysis operation receives data from a
plurality of crowd sourced devices, aggregates the data received
from the plurality of crowd sourced devices and maps the data
received from the plurality of crowd sourced devices to a cohort
(i.e., a group of individuals used in a study who have something in
common). In certain embodiments, the crowdsourcing data analysis
operation analyzes the data received from the plurality of crowd
sourced devices to provide a deterministic analysis to infer a
likelihood of potential incidents related to a group of individuals
at any given time. Additionally in certain embodiments, the mapping
of the data received from the plurality of crowd sourced devices
includes binding the data to a physical or logical location.
Inventors: |
Cunico; Hernan A.; (Holly
Springs, NC) ; Dunne; Jonathan; (Dungarvan, IE)
; O'Connor; Jeremiah; (Roscommon, IE) ; Silva;
Asima; (Holden, MA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
International Business Machines Corporation |
Armonk |
NY |
US |
|
|
Family ID: |
57684234 |
Appl. No.: |
15/138518 |
Filed: |
April 26, 2016 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
|
14754971 |
Jun 30, 2015 |
|
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15138518 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G06F 16/2477 20190101;
H04W 4/023 20130101; H04W 4/025 20130101; H04L 67/22 20130101; G06F
16/29 20190101; H04W 4/90 20180201; G06F 16/9535 20190101; G06F
16/285 20190101 |
International
Class: |
G06F 17/30 20060101
G06F017/30 |
Claims
1. A computer-implementable method for performing a crowdsourcing
data analysis operation, comprising: receiving data from a
plurality of crowd sourced devices; aggregating the data received
from the plurality of crowd sourced devices; and, mapping the data
received from the plurality of crowd sourced devices to a
cohort.
2. The method of claim 1, further comprising: analyzing the data
received from the plurality of crowd sourced devices to provide a
deterministic analysis of the data received from the plurality of
crowd sourced devices, the deterministic analysis enabling an
inference of a likelihood of potential incidents related to a group
of individuals at any given time.
3. The method of claim 2, further comprising: presenting a visual
cue to illustrate the analysis of the data received from the
plurality of crowd sourced data via a graphical representation.
4. The method of claim 1, wherein: the mapping of the data received
from the plurality of crowd sourced devices includes binding the
data to a venue.
5. The method of claim 4, wherein: the venue comprises at least one
of a physical location and a logical location.
6. The method of claim 4, further comprising: registering each of
the plurality of crowd sourced devices as each device enters the
venue; and, deregistering each of the plurality of crowd sourced
devices as each device exits the venue.
Description
BACKGROUND OF THE INVENTION
[0001] Field of the Invention
[0002] The present invention relates to information handling
systems. More specifically, embodiments of the invention relate to
crowd sourcing of device sensor data for real time response.
[0003] Description of the Related Art
[0004] Early detection of events can be challenging, especially
when the event occurs in a public venue and crowds are involved. It
can be especially desirable to detect negative events in such a
situation. Early detection of negative events, or identifying the
precursors for such events can help in preventing the occurrence of
the negative event. Additionally, should such an event occur, early
detection of the event can significantly reduce any negative
impacts of the events and aid in a prompt response to the event.
For example, with a performance such as a music concert in a large
venue, early detection of a crowd moving towards exits of the venue
at an unexpected time can provide an indication of panic. Such a
detection might alert the venue staff to open additional escape
routes which in turn would likely help in minimizing injuries or
even loss of life.
[0005] It is known to use a single sensor to detect the occurrence
of an event associated with the single sensor. For example,
services are available to detect if a user falls and if so to
trigger an emergency response. Also, it is known to integrate
sensors into automobiles such that if an accident is detected (such
as via sensing that an airbag has been deployed), location data and
service request are automatically sent to an appropriate emergency
service.
[0006] However, in a use case where a plurality of users are
located in a specific location, it would be desirable to monitor
not only telemetry data of the individual, but also of the larger
cohort in real time to allow inference of potential incidents which
may affect a wider group rather than the individual. It would also
be desirable to provide an ability to infer a likelihood of minor,
major and critical events from collected telemetry data. It would
also be desirable to provide an early warning system to mitigate
the consequences of potentially negative events.
SUMMARY OF THE INVENTION
[0007] A system, method, and computer-readable medium are disclosed
for performing a crowdsourcing data analysis operation. More
specifically, the crowdsourcing data analysis operation receives
data from a plurality of crowd sourced devices, aggregates the data
received from the plurality of crowd sourced devices and maps the
data received from the plurality of crowd sourced devices to a
cohort (i.e., a group of individuals used in a study who have
something in common). In certain embodiments, the crowdsourcing
data analysis operation analyzes the data received from the
plurality of crowd sourced devices to provide a deterministic
analysis to infer a likelihood of potential incidents related to a
group of individuals at any given time. Additionally in certain
embodiments, the mapping of the data received from the plurality of
crowd sourced devices includes binding the data to a physical or
logical location. In certain embodiments, the logical location
comprises a social event. Additionally, in certain embodiments, the
crowdsourcing data analysis operation includes presenting a visual
cue to illustrate the analysis of the data received from the
plurality of crowd sourced data via a graphical representation.
BRIEF DESCRIPTION OF THE DRAWINGS
[0008] The present invention may be better understood, and its
numerous objects, features and advantages made apparent to those
skilled in the art by referencing the accompanying drawings. The
use of the same reference number throughout the several figures
designates a like or similar element.
[0009] FIG. 1 shows a schematic diagram of a question
prioritization system.
[0010] FIG. 2 shows a block diagram of a data processing
system.
[0011] FIG. 3 shows a block diagram of a crowd sourcing
environment.
[0012] FIG. 4 shows a flow chart of the operation of a crowd
sourcing device analysis system.
[0013] FIG. 5 shows a graphical representation of the operation of
a crowd sourcing device analysis system in a venue.
[0014] FIG. 6 shows another graphical representation of the
operation of a crowd sourcing device analysis system in a
venue.
DETAILED DESCRIPTION
[0015] The present invention may be a system, a method, and/or a
computer program product. In addition, selected aspects of the
present invention may take the form of an entirely hardware
embodiment, an entirely software embodiment (including firmware,
resident software, micro-code, etc.) or an embodiment combining
software and/or hardware aspects that may all generally be referred
to herein as a "circuit," "module" or "system." Furthermore,
aspects of the present invention may take the form of computer
program product embodied in 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.
[0016] 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 dynamic or static random access memory (RAM), a read-only memory
(ROM), an erasable programmable read-only memory (EPROM or Flash
memory), a magnetic storage device, 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.
[0017] 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.
[0018] 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 Java, 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 or cluster of servers. 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.
[0019] 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.
[0020] 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.
[0021] 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.
[0022] 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.
[0023] FIG. 1 depicts a schematic diagram of one illustrative
embodiment of a question prioritization system 10 and
question/answer (QA) system 100 connected to a computer network
140. The QA system 100 includes a knowledge manager 104 that is
connected to a knowledge base 106 and configured to provide
question/answer (QA) generation functionality for one or more
content users who submit across the network 140 to the QA system
100. To assist with efficient sorting and presentation of questions
to the QA system 100, the prioritization system 10 may be connected
to the computer network 140 to receive user questions, and may
include a plurality of subsystems which interact with cognitive
systems, like the knowledge manager 100, to prioritize questions or
requests being submitted to the knowledge manager 100.
[0024] The Named Entity subsystem 12 receives and processes each
question 11 by using natural language (NL) processing to analyze
each question and extract question topic information contained in
the question, such as named entities, phrases, urgent terms, and/or
other specified terms which are stored in one or more domain entity
dictionaries 13. By leveraging a plurality of pluggable domain
dictionaries relating to different domains or areas (e.g., travel,
healthcare, electronics, game shows, financial services), the
domain dictionary 11 enables critical and urgent words (e.g.,
"threat level") from different domains (e.g., "travel") to be
identified in each question based on their presence in the domain
dictionary 11. To this end, the Named Entity subsystem 12 may use a
Natural Language Processing (NLP) routine to identify the question
topic information in each question. As used herein, "NLP" refers to
the field of computer science, artificial intelligence, and
linguistics concerned with the interactions between computers and
human (natural) languages. In this context, NLP is related to the
area of human-computer interaction and natural language
understanding by computer systems that enable computer systems to
derive meaning from human or natural language input. For example,
NLP can be used to derive meaning from a human-oriented question
such as, "What is tallest mountain in North America?" and to
identify specified terms, such as named entities, phrases, or
urgent terms contained in the question. The process identifies key
terms and attributes in the question and compares the identified
terms to the stored terms in the domain dictionary 13.
[0025] The Question Priority Manager subsystem 14 performs
additional processing on each question to extract question context
information 15A. In addition or in the alternative, the Question
Priority Manager subsystem 14 may also extract server performance
information 15B for the question prioritization system 10 and/or QA
system 100. In selected embodiments, the extracted question context
information 15A may include data that identifies the user context
and location when the question was submitted or received. For
example, the extracted question context information 15A may include
data that identifies the user who submitted the question (e.g.,
through login credentials), the device or computer which sent the
question, the channel over which the question was submitted, the
location of the user or device that sent the question, any special
interest location indicator (e.g., hospital, public-safety
answering point, etc.), or other context-related data for the
question. The Question Priority Manager subsystem 14 may also
determine or extract selected server performance data 15B for the
processing of each question. In selected embodiments, the server
performance information 15B may include operational metric data
relating to the available processing resources at the question
prioritization system 10 and/or QA system 100, such as operational
or run-time data, CPU utilization data, available disk space data,
bandwidth utilization data, etc. As part of the extracted
information 15A/B, the Question Priority Manager subsystem 14 may
identify the SLA or QoS processing requirements that apply to the
question being analyzed, the history of analysis and feedback for
the question or submitting user, and the like. Using the question
topic information and extracted question context and/or server
performance information, the Question Priority Manager subsystem 14
is configured to populate feature values for the Priority
Assignment Model 16 which provides a machine learning predictive
model for generating a target priority values for the question,
such as by using an artificial intelligence (AI) rule-based logic
to determine and assign a question urgency value to each question
for purposes of prioritizing the response processing of each
question by the QA system 100.
[0026] The Prioritization Manager subsystem 17 performs additional
sort or rank processing to organize the received questions based on
at least the associated target priority values such that high
priority questions are put to the front of a prioritized question
queue 18 for output as prioritized questions 19. In the question
queue 18 of the Prioritization Manager subsystem 17, the highest
priority question is placed at the front for delivery to the
assigned QA system 100. In selected embodiments, the prioritized
questions 19 from the Prioritization Manager subsystem 17 that have
a specified target priority value may be assigned to a specific
pipeline (e.g., QA System 100A) in the QA system cluster 100. As
will be appreciated, the Prioritization Manager subsystem 17 may
use the question queue 18 as a message queue to provide an
asynchronous communications protocol for delivering prioritized
questions 19 to the QA system 100 such that the Prioritization
Manager subsystem 17 and QA system 100 do not need to interact with
a question queue 18 at the same time by storing prioritized
questions in the question queue 18 until the QA system 100
retrieves them. In this way, a wider asynchronous network supports
the passing of prioritized questions as messages between different
computer systems 100A, 100B, connecting multiple applications and
multiple operating systems. Messages can also be passed from queue
to queue in order for a message to reach the ultimate desired
recipient. An example of a commercial implementation of such
messaging software is IBM's Web Sphere MQ (previously MQ Series).
In selected embodiments, the organizational function of the
Prioritization Manager subsystem 17 may be configured to convert
over-subscribing questions into asynchronous responses, even if
they were asked in a synchronized fashion.
[0027] The QA system 100 may include one or more QA system
pipelines 100A, 100B, each of which includes a computing device 104
(comprising one or more processors and one or more memories, and
potentially any other computing device elements generally known in
the art including buses, storage devices, communication interfaces,
and the like) for processing questions received over the network
140 from one or more users at computing devices (e.g., 110, 120,
130) connected over the network 140 for communication with each
other and with other devices or components via one or more wired
and/or wireless data communication links, where each communication
link may comprise one or more of wires, routers, switches,
transmitters, receivers, or the like. In this networked
arrangement, the QA system 100 and network 140 may enable
question/answer (QA) generation functionality for one or more
content users. Other embodiments of QA system 100 may be used with
components, systems, sub-systems, and/or devices other than those
that are depicted herein.
[0028] In each QA system pipeline 100A, 100B, a prioritized
question 19 is received and prioritized for processing to generate
an answer 20. In sequence, prioritized questions 19 are dequeued
from the shared question queue 18, from which they are de-queued by
the pipeline instances for processing in priority order rather than
insertion order. In selected embodiments, the question queue 18 may
be implemented based on a "priority heap" data structure. During
processing within a QA system pipeline (e.g., 100A), questions may
be split into many subtasks which run concurrently. A single
pipeline instance can process a number of questions concurrently,
but only a certain number of subtasks. In addition, each QA system
pipeline may include a prioritized queue (not shown) to manage the
processing order of these subtasks, with the top-level priority
corresponding to the time that the corresponding question started
(earliest has highest priority). However, it will be appreciated
that such internal prioritization within each QA system pipeline
may be augmented by the external target priority values generated
for each question by the Question Priority Manager subsystem 14 to
take precedence or ranking priority over the question start time.
In this way, more important or higher priority questions can "fast
track" through the QA system pipeline if it is busy with
already-running questions.
[0029] In the QA system 100, the knowledge manager 104 may be
configured to receive inputs from various sources. For example,
knowledge manager 104 may receive input from the question
prioritization system 10, network 140, a knowledge base or corpus
of electronic documents 106 or other data, a content creator 108,
content users, and other possible sources of input. In selected
embodiments, some or all of the inputs to knowledge manager 104 may
be routed through the network 140 and/or the question
prioritization system 10. The various computing devices (e.g., 110,
120, 130, 150, 160, 170) on the network 140 may include access
points for content creators and content users. Some of the
computing devices may include devices for a database storing the
corpus of data as the body of information used by the knowledge
manager 104 to generate answers to cases. The network 140 may
include local network connections and remote connections in various
embodiments, such that knowledge manager 104 may operate in
environments of any size, including local and global, e.g., the
Internet. Additionally, knowledge manager 104 serves as a front-end
system that can make available a variety of knowledge extracted
from or represented in documents, network-accessible sources and/or
structured data sources. In this manner, some processes populate
the knowledge manager with the knowledge manager also including
input interfaces to receive knowledge requests and respond
accordingly.
[0030] In one embodiment, the content creator creates content in a
document 106 for use as part of a corpus of data with knowledge
manager 104. The document 106 may include any file, text, article,
or source of data (e.g., scholarly articles, dictionary
definitions, encyclopedia references, and the like) for use in
knowledge manager 104. Content users may access knowledge manager
104 via a network connection or an Internet connection to the
network 140, and may input questions to knowledge manager 104 that
may be answered by the content in the corpus of data. As further
described below, when a process evaluates a given section of a
document for semantic content, the process can use a variety of
conventions to query it from the knowledge manager. One convention
is to send a well-formed question. Semantic content is content
based on the relation between signifiers, such as words, phrases,
signs, and symbols, and what they stand for, their denotation, or
connotation. In other words, semantic content is content that
interprets an expression, such as by using Natural Language (NL)
Processing. In one embodiment, the process sends well-formed
questions (e.g., natural language questions, etc.) to the knowledge
manager. Knowledge manager 104 may interpret the question and
provide a response to the content user containing one or more
answers to the question. In some embodiments, knowledge manager 104
may provide a response to users in a ranked list of answers.
[0031] In some illustrative embodiments, QA system 100 may be the
IBM Watson.TM. QA system available from International Business
Machines Corporation of Armonk, N.Y., which is augmented with the
mechanisms of the illustrative embodiments described hereafter. The
IBM Watson.TM. knowledge manager system may receive an input
question which it then parses to extract the major features of the
question, that in turn are then used to formulate queries that are
applied to the corpus of data. Based on the application of the
queries to the corpus of data, a set of hypotheses, or candidate
answers to the input question, are generated by looking across the
corpus of data for portions of the corpus of data that have some
potential for containing a valuable response to the input
question.
[0032] The IBM Watson.TM. QA system then performs deep analysis on
the language of the input prioritized question 19 and the language
used in each of the portions of the corpus of data found during the
application of the queries using a variety of reasoning algorithms.
There may be hundreds or even thousands of reasoning algorithms
applied, each of which performs different analysis, e.g.,
comparisons, and generates a score. For example, some reasoning
algorithms may look at the matching of terms and synonyms within
the language of the input question and the found portions of the
corpus of data. Other reasoning algorithms may look at temporal or
spatial features in the language, while others may evaluate the
source of the portion of the corpus of data and evaluate its
veracity.
[0033] The scores obtained from the various reasoning algorithms
indicate the extent to which the potential response is inferred by
the input question based on the specific area of focus of that
reasoning algorithm. Each resulting score is then weighted against
a statistical model. The statistical model captures how well the
reasoning algorithm performed at establishing the inference between
two similar passages for a particular domain during the training
period of the IBM Watson.TM. QA system. The statistical model may
then be used to summarize a level of confidence that the IBM
Watson.TM. QA system has regarding the evidence that the potential
response, i.e. candidate answer, is inferred by the question. This
process may be repeated for each of the candidate answers until the
IBM Watson.TM. QA system identifies candidate answers that surface
as being significantly stronger than others and thus, generates a
final answer, or ranked set of answers, for the input question. The
QA system 100 then generates an output response or answer 20 with
the final answer and associated confidence and supporting evidence.
More information about the IBM Watson.TM. QA system may be
obtained, for example, from the IBM Corporation website, IBM
Redbooks, and the like. For example, information about the IBM
Watson.TM. QA system can be found in Yuan et al., "Watson and
Healthcare," IBM developerWorks, 2011 and "The Era of Cognitive
Systems: An Inside Look at IBM Watson and How it Works" by Rob
High, IBM Redbooks, 2012.
[0034] Types of information handling systems that can utilize QA
system 100 range from small handheld devices, such as handheld
computer/mobile telephone 110 to large mainframe systems, such as
mainframe computer 170. Examples of handheld computer 110 include
personal digital assistants (PDAs), personal entertainment devices,
such as MP3 players, portable televisions, and compact disc
players. Other examples of information handling systems include
pen, or tablet, computer 120, laptop, or notebook, computer 130,
personal computer system 150, and server 160. As shown, the various
information handling systems can be networked together using
computer network 140. Types of computer network 140 that can be
used to interconnect the various information handling systems
include Local Area Networks (LANs), Wireless Local Area Networks
(WLANs), the Internet, the Public Switched Telephone Network
(PSTN), other wireless networks, and any other network topology
that can be used to interconnect the information handling systems.
Many of the information handling systems include nonvolatile data
stores, such as hard drives and/or nonvolatile memory. Some of the
information handling systems may use separate nonvolatile data
stores (e.g., server 160 utilizes nonvolatile data store 165, and
mainframe computer 170 utilizes nonvolatile data store 175). The
nonvolatile data store can be a component that is external to the
various information handling systems or can be internal to one of
the information handling systems. An illustrative example of an
information handling system showing an exemplary processor and
various components commonly accessed by the processor is shown in
FIG. 2.
[0035] FIG. 2 illustrates an information handling system 202, more
particularly, a processor and common components, which is a
simplified example of a computer system capable of performing the
computing operations described herein. Information handling system
202 includes a processor unit 204 that is coupled to a system bus
206. A video adapter 208, which controls a display 210, is also
coupled to system bus 206. System bus 206 is coupled via a bus
bridge 212 to an Input/Output (I/O) bus 214. An I/O interface 216
is coupled to I/O bus 214. The I/O interface 216 affords
communication with various I/O devices, including a keyboard 218, a
mouse 220, a Compact Disk--Read Only Memory (CD-ROM) drive 222, a
floppy disk drive 224, and a flash drive memory 226. The format of
the ports connected to I/O interface 216 may be any known to those
skilled in the art of computer architecture, including but not
limited to Universal Serial Bus (USB) ports.
[0036] The information handling system 202 is able to communicate
with a service provider server 252 via a network 228 using a
network interface 230, which is coupled to system bus 206. Network
228 may be an external network such as the Internet, or an internal
network such as an Ethernet Network or a Virtual Private Network
(VPN). Using network 228, client computer 202 is able to use the
present invention to access service provider server 252.
[0037] A hard drive interface 232 is also coupled to system bus
206. Hard drive interface 232 interfaces with a hard drive 234. In
a preferred embodiment, hard drive 234 populates a system memory
236, which is also coupled to system bus 206. Data that populates
system memory 236 includes the information handling system's 202
operating system (OS) 238 and software programs 244.
[0038] OS 238 includes a shell 240 for providing transparent user
access to resources such as software programs 244. Generally, shell
240 is a program that provides an interpreter and an interface
between the user and the operating system. More specifically, shell
240 executes commands that are entered into a command line user
interface or from a file. Thus, shell 240 (as it is called in
UNIX.RTM.), also called a command processor in Windows.RTM., is
generally the highest level of the operating system software
hierarchy and serves as a command interpreter. The shell provides a
system prompt, interprets commands entered by keyboard, mouse, or
other user input media, and sends the interpreted command(s) to the
appropriate lower levels of the operating system (e.g., a kernel
242) for processing. While shell 240 generally is a text-based,
line-oriented user interface, the present invention can also
support other user interface modes, such as graphical, voice,
gestural, etc.
[0039] As depicted, OS 238 also includes kernel 242, which includes
lower levels of functionality for OS 238, including essential
services required by other parts of OS 238 and software programs
244, including memory management, process and task management, disk
management, and mouse and keyboard management. Software programs
244 may include a browser 246 and email client 248. Browser 246
includes program modules and instructions enabling a World Wide Web
(WWW) client (i.e., information handling system 202) to send and
receive network messages to the Internet using HyperText Transfer
Protocol (HTTP) messaging, thus enabling communication with service
provider server 252. In various embodiments, software programs 244
may also include a crowd sourcing analysis module 250. In these and
other embodiments, the crowd sourcing analysis module 250 includes
code for implementing the processes described hereinbelow. In one
embodiment, information handling system 202 is able to download the
crowd sourcing analysis module 250 from a service provider server
252.
[0040] The hardware elements depicted in the information handling
system 202 are not intended to be exhaustive, but rather are
representative to highlight components used by the present
invention. For instance, the information handling system 202 may
include alternate memory storage devices such as magnetic
cassettes, Digital Versatile Disks (DVDs), Bernoulli cartridges,
and the like. These and other variations are intended to be within
the spirit, scope and intent of the present invention.
[0041] FIG. 3 is a block diagram of a crowd sourcing analysis
environment 300 implemented in accordance with an embodiment of the
invention. In various embodiments, the operation of a plurality of
devices are monitored to perform a crowdsourcing data analysis
operation. In various embodiments, a crowd sourcing analysis system
301 is implemented to execute on a user device 304 and to perform a
crowd sourcing analysis operation within the crowd sourcing
analysis environment 300. In certain embodiments, the crowd
sourcing analysis operation may be performed as a hardware
operation, a software operation, or a combination thereof. In
certain embodiments, the crowd sourcing analysis system 301
includes some or all of the functions performed by the crowd
sourcing analysis module 250.
[0042] The crowdsourcing data analysis operation receives data from
the plurality of crowd sourced devices, aggregates the data
received from the plurality of crowd sourced devices and maps the
data received from the plurality of crowd sourced devices to a
cohort (i.e., a group of individuals used in a study who have
something in common). In certain embodiments, the crowdsourcing
data analysis operation analyzes the data received from the
plurality of crowd sourced devices to provide a deterministic
analysis to infer a likelihood of potential incidents related to a
group of individuals at any given time. Additionally in certain
embodiments, the mapping of the data received from the plurality of
crowd sourced devices includes binding the data to a physical or
logical location. In certain embodiments, the logical location
comprises a social event. Additionally, in certain embodiments, the
crowdsourcing data analysis operation includes presenting a visual
cue to illustrate the analysis of the data received from the
plurality of crowd sourced data via a graphical representation.
[0043] As used herein, a user device 304 refers to an information
handling system such as a personal computer, a laptop computer, a
tablet computer, a personal digital assistant (PDA), a smart phone,
a mobile telephone, or other device that is capable of
communicating and processing data. In various embodiments, the user
device can include one or more analysis applications 306. In
various embodiments, the user device 304 includes a repository of
crowd sourcing data 308. Also, in certain embodiments the
repository of crowd sourcing data 308 includes a crowd sourcing
data repository. In certain embodiments, the crowd sourcing data
repository may include a crowd sourcing database. Also, in certain
embodiments, the crowd sourcing analysis system 301 and the crowd
sourcing data 308 may be physically disparate. Also, in certain
embodiments, the crowd sourcing analysis system 301 may include a
crowd sourcing device agent which executes elsewhere within the
crowd sourcing analysis environment 300. Skilled practitioners of
the art will realize that many such embodiments are possible and
the foregoing is not intended to limit the spirit, scope or intent
of the invention.
[0044] In various embodiments, the user device 304 is used to
communicate data between the crowd sourcing analysis system 301 and
a master crowd sourcing analysis data system 322, described in
greater detail herein, through the use of a network 140. In certain
embodiments, the master crowd sourcing analysis data system 322
includes a repository of master crowd sourcing analysis data 324,
likewise described in greater detail herein. In certain
embodiments, the master crowd sourcing analysis data 324 is used
when performing a crowd sourcing analysis operation of received
crowd sourced data. For example, in certain embodiments, the master
crowd sourcing analysis data 324 may include data relating to a
plurality of venues as well as typical and atypical behaviors
associated with each of the plurality of venues.
[0045] In various embodiments, the master crowd sourcing analysis
data system 322 can include one or more of a relations database
management system (RDBMS), a data warehouse, and a not only
structure query language (NoSQL) database. Also, in various
embodiments, the master crowd sourcing analysis data system 322 can
include one or more cloud based databases (e.g., Cloud DB1, Cloud
DB2, Cloud DB3, etc.) Skilled practitioners of the art will realize
that many such embodiments are possible and the foregoing is not
intended to limit the spirit, scope or intent of the invention.
[0046] In various embodiments, the network 140 may be a public
network, such as the Internet, a physical private network, a
virtual private network (VPN), or any combination thereof. In
certain embodiments, the network 140 may be a wireless network,
including a personal area network (PAN), based on technologies such
as Bluetooth or Ultra Wideband (UWB). In various embodiments, the
wireless network may include a wireless local area network (WLAN),
based on variations of the IEEE 802.11 specification, often
referred to as WiFi. In certain embodiments, the wireless network
may include a wireless wide area network (WWAN) based on an
industry standard including various 3G technologies, including
evolution-data optimized (EVDO), IEEE 802.16 (WiMAX), wireless
broadband (WiBro), high-speed downlink packet access (HSDPA),
high-speed uplink packet access (HSUPA), and emerging fourth
generation (4G) wireless technologies. Skilled practitioners of the
art will realize that many such embodiments are possible and the
foregoing is not intended to limit the spirit, scope or intent of
the invention.
[0047] As used herein, a venue profile broadly refers to a profile
of a venue (or plurality of venues such as related venues) that can
be used as a reference for predicted incidents relating to a
particular venue. In various embodiments, the venue profile may be
generated based upon data that are crowdsourced from a plurality of
devices, such as crowdsourced devices `1` 326 through `n` 328, some
or all of which are located within a venue 329. As used herein,
crowdsourcing broadly refers to the process of obtaining needed
services, content or other information by soliciting contributions
from a group of users, devices or systems. Skilled practitioners of
the art will be aware that crowdsourcing is often used to subdivide
tedious tasks, processes or operations across multiple
contributors, each of which adds a portion of value to the greater
result. In various embodiments, each of the crowdsourced devices
`1` 326 through `n` 328 provides their respective data to the crowd
sourcing analysis system 301 as well as the master crowd sourcing
analysis data system 322. Once received, they are stored in the
repository of venue profile data 308. In various embodiments, the
network 140 is used by the crowdsourced devices `1` 326 through `n`
328 to respectively provide their data to the device 304. In
various embodiments, the crowd sourced data is also stored in the
master crowd sourcing analysis data repository 324.
[0048] Ongoing operations are then performed to monitor data
generated via the crowdsourced devices as well as other devices
accessing the master crowd sourcing data analysis system 322.
Skilled practitioners of the art will recognize that many methods
for monitoring queries are possible and the foregoing is not
intended to limit the spirit, scope or intent of the invention.
[0049] Ongoing operations are then performed to store the crowd
sourced data as it is collected for subsequent comparison and
analysis. The method by which the crowd sourced data is stored, and
the format in which it is stored, is a matter of design choice. In
various embodiments, the collected data is stored in the repository
of crowd sourced data 308. In certain embodiments, a subset of the
collected data is stored in the repository of data 308. For
example, data associated with an `n` number of users of the
environment may be selected for storage in the repository of data
308 where the users selected for storage may have certain
characteristics relevant to the venue analysis. For example, in
certain embodiments, the users for which the data is stored (and
potentially analyzed) may be located within a particular
sub-portion of the venue. Skilled practitioners of the art will
recognize that many methods for identifying a number of users for
analysis are possible and the foregoing is not intended to limit
the spirit, scope or intent of the invention.
[0050] FIG. 4 shows a flow chart of the operation 400 of a crowd
sourcing device analysis system. More specifically, the operation
begins at step 410 with individuals having respective data
generating devices entering a venue of interest. For example, the
venue of interest could correspond to an arena in which a
performance or sporting event is to occur. In various embodiments,
the individuals are provided with a device such as a wristband or
lanyard which contains the device for generating the crowdsourced
data. In other embodiments, the device may correspond to a user
device as described herein.
[0051] Next, at step 420, each crowdsourced device 329 registers
with a crowd sourcing data analysis system 301. In certain
embodiments, the crowd sourcing data analysis system 301 may be
contained either locally or remotely within an overall building
management system. Additionally, in various embodiments, the
crowdsourced device may include additional identification
information which is provided to the crowd sourcing data analysis
system 301 when the device is registered. This additional
identification information can indicate whether the individual
associated with the crowdsourced device 301 could potentially
require special attention such as whether the individual may be
associated with certain business, medical, political or historical
criteria. For example, the individual associated with the
crowdsourced device 329 may have a medical condition such as
epilepsy or may be a preferred guest for whom guidance to preferred
seating may be indicated.
[0052] Next, at step 430, the crowd sourcing data analysis system
301 initiates tracking of the plurality of crowd sourced devices
301. Each of the crowd sourced devices provides device data
relating to the individual associated with the crowd sourced data.
This device data includes one or more of a device location, a
device speed, and a device height. In certain embodiments, the
device speed of travel is provided as part of the device data. The
device location and device speed can also be used to generate a
device direction and speed of travel. Additionally in certain
embodiments, the device data can also include temperature and light
intensity data. Also, in certain embodiments, the device data can
include medical data of the individual associated with the device
such as heart rate and blood pressure. Certain crowd sourced
devices may be considered enhanced crowd sourced devices if the
device provides more device data than other devices. In certain
applications, enhanced crowd sourced devices might be provided to
individuals according to certain criteria. For example, individuals
with particular health conditions or certain status (e.g., VIP's)
may be provided enhanced crowd sourced devices while other
individuals are provided with crowd sourced devices which provide a
subset of the data provided by the enhanced crowd sourced
devices.
[0053] Next, at step 440, the crowd sourcing data analysis system
301 aggregates the crowd sourced data and performs an analysis
based upon the crowd sourced data. This analysis includes automatic
monitoring for certain conditions and generating alerts when
certain conditions are detected. For example, the monitoring might
include determining whether concentrations of devices rise above
threshold levels. The thresholds might be related to a location
within the venue, such that if more than a certain number of
individuals are within a predetermined area of the venue then an
alert is generated. Also, for example, the monitoring might include
determining when device speeds rise over a threshold for a given
number of devices. Also for example, the monitoring might include
determining when device heights drop below a threshold for a give
number of devices. Providing the monitoring and alerts allows the
crowd sourcing data analysis system 301 (or the event staff) to
proactively address issues in real time. For example, the crowd
sourcing data analysis system 301 might automatically turn on
emergency systems such as emergency light or sprinkler systems in
response to a particular alert. The crowd sourcing data analysis
system 301 might also automatically open emergency exits and/or
close fire doors in response to a particular alert. The crowd
sourcing data analysis system 301 might also automatically request
emergency assistance in response to a particular alert.
[0054] Additionally, in certain embodiments, the analysis tailored
to the data provided by the crowd sourced devices. For example, if
the crowd sourced devices provided temperature data, then the crowd
sourced data analysis system 301 could perform a heat map analysis
using the temperature provided by the plurality of crowd sourced
devices.
[0055] Next, at step 450, after the event is completed, the crowd
sourced devices are unregistered. In certain embodiments when the
crowd sourced devices were provided by the event organizers, the
crowd sourced devices may also be reclaimed upon completion of the
event.
[0056] Next, at step 460, the crowd sourcing data analysis system
301 performs a post event analysis of the crowd sourced data
received during the event. This post event analysis may be used to
provide recommendations for future events. This post event analysis
may also be performed by the master crowd sourcing data analysis
system 322 and stored to the master crowd sourcing analysis data
repository 324 for use in generating more accurate analyses of
future events across a plurality of disparate venues which use the
crowd sourcing data analysis system 301.
[0057] FIG. 5 shows a graphical representation 500 of the operation
of a crowd sourcing device analysis system in a venue. FIG. 6 shows
another graphical representation 600 of the operation of a crowd
sourcing device analysis system in a venue.
[0058] More specifically, a representation of the venue 510
includes representations of relevant items within the venue such as
a representation of a stage 520 as well as representations of each
of a plurality of exits 530. Information relating to the venue is
stored within the crowd sourcing data analysis system 301. In
certain embodiments, the information includes physical information
such as dimensions of the venue as well as safety information such
as safety equipment (such as fire alarms, sprinkler systems, etc.)
included within the venue. In certain embodiments, the information
of the venue is downloaded from the master crowd sourcing data
analyses system 322. In certain embodiments, information gathered
from similar venues (e.g., venues having substantially (e.g.,
+/-10%) the same physical size, substantially (e.g., +/-10%) the
same capacity or a similar layout is also provided to the crowd
sourcing data analysis system 301.
[0059] Some or all of the individuals 540 attending the event at
the venue have associated crowd sourced data devices. When
performing the analysis of the event, if there are no potential
issues detected from the analysis of the data received from the
plurality of crowd sourced data individuals within a venue are
represented as green dots on the representation of the venue 510.
If some individuals are exhibiting some negative behavior as
recorded or indicated by their sensor data, the presentation
corresponding to those individuals is represented as yellow dots
(e.g., individuals 610). If a serious problem with a cohort or
group is detected based upon the analysis of the crowd sourced data
from a plurality of individuals, then the presentation
corresponding to those individuals is represented as red (e.g.,
individuals 620). In certain embodiments, because more individuals
would likely be involved in the serious problem, the red
presentation is also more visible because more dots are represented
as red. Also, in certain embodiments, an additional warning (such
as flashing of the red dots or a separate warning indication which
could include a visual presentation and/or an audio indication) is
generated. For example, in the example shown in FIG. 6 an
additional warning 635 is generated with respect to the exits 630
because the crowd sourced device data is indicating that the
patrons are bunching at these exits and none have passed through
the exits, indicating an additional potential problem with these
portions of the venue.
[0060] The present invention is well adapted to attain the
advantages mentioned as well as others inherent therein. While the
present invention has been depicted, described, and is defined by
reference to particular embodiments of the invention, such
references do not imply a limitation on the invention, and no such
limitation is to be inferred. The invention is capable of
considerable modification, alteration, and equivalents in form and
function, as will occur to those ordinarily skilled in the
pertinent arts. The depicted and described embodiments are examples
only, and are not exhaustive of the scope of the invention.
[0061] Consequently, the invention is intended to be limited only
by the spirit and scope of the appended claims, giving full
cognizance to equivalents in all respects.
* * * * *