U.S. patent application number 15/811727 was filed with the patent office on 2018-05-10 for online social interaction, education, and health care by analysing affect and cognitive features.
The applicant listed for this patent is Abhishek BISWAS, Neil S. DAVEY, Sonya DAVEY. Invention is credited to Abhishek BISWAS, Neil S. DAVEY, Sonya DAVEY.
Application Number | 20180131733 15/811727 |
Document ID | / |
Family ID | 49213367 |
Filed Date | 2018-05-10 |
United States Patent
Application |
20180131733 |
Kind Code |
A1 |
DAVEY; Neil S. ; et
al. |
May 10, 2018 |
ONLINE SOCIAL INTERACTION, EDUCATION, AND HEALTH CARE BY ANALYSING
AFFECT AND COGNITIVE FEATURES
Abstract
A method of establishing a collaborative platform comprising
performing a collaborative interactive session for a plurality of
members, and analysing affect and/or cognitive features of some or
all of the plurality of members, wherein some or all of the
plurality of members from different human interaction platforms
interact via the collaborative platform, wherein the affect
comprises an experience of feeling or emotion, and wherein the
cognitive features comprise features in a cognitive state, the
cognitive state comprising a state of an internal mental
process.
Inventors: |
DAVEY; Neil S.;
(Gaithersburg, MD) ; DAVEY; Sonya; (Gaithersburg,
MD) ; BISWAS; Abhishek; (Calcutta, IN) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
DAVEY; Neil S.
DAVEY; Sonya
BISWAS; Abhishek |
Gaithersburg
Gaithersburg
Calcutta |
MD
MD |
US
US
IN |
|
|
Family ID: |
49213367 |
Appl. No.: |
15/811727 |
Filed: |
November 14, 2017 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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13668337 |
Nov 5, 2012 |
9819711 |
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15811727 |
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61720405 |
Oct 31, 2012 |
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61719980 |
Oct 30, 2012 |
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61625949 |
Apr 18, 2012 |
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61556205 |
Nov 5, 2011 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
H04L 51/32 20130101;
H04L 12/1813 20130101; H04L 65/403 20130101 |
International
Class: |
H04L 29/06 20060101
H04L029/06; H04L 12/18 20060101 H04L012/18; H04L 12/58 20060101
H04L012/58 |
Claims
1. A method of establishing a collaborative platform comprising:
performing a collaborative interactive session among a plurality of
members, analyzing affect and cognitive features of the plurality
of members by obtaining video images from each of the plurality of
members, wherein video images comprise pupil dilation, eye tracking
and/or facial features of each of the plurality of the members,
analyzing in real-time the video images using a video analysis
system, presenting questions to each of the plurality of the
members during a sub-segment of the collaborative interactive
session, and starting a next sub-segment of the collaborative
interactive session only after a minimum number of the questions
are completed by the plurality of the members.
2. The method of claim 1, wherein the plurality of members from
different human interaction platforms interact via the
collaborative platform,
3. The method of claim 1, further comprising displaying of targeted
advertisements or notifications based on the context of the
interactive collaborative session.
4. The method of claim 3, further comprising measuring
effectiveness of the displaying of targeted advertisements or
notifications.
5. The method of claim 1, further comprising integrating an
application or a device within the collaborative interactive
session.
6. A computer implemented system comprising: a storage medium
configured to store data from the collaborative platform of claim
1; and a processor configured to perform a collaborative
interactive session for a plurality of members, wherein the system
analyses affect and cognitive features of the plurality of
members.
7. The system of claim 6, wherein the plurality of members from
different human interaction platforms interact via the
collaborative interactive session, wherein the different human
interactions platforms comprise social media platforms.
8. The system of claim 6, wherein the system is further configured
to display targeted advertisements or notifications based on the
context of the interactive collaborative sessions.
9. The system of claim 8, wherein the system is further configured
to measure effectiveness of the displaying of targeted
advertisements or notifications.
10. The system of claim 6, wherein the system is further configured
to integrate an application or a device within the collaborative
interactive session.
11. The system of claim 6, wherein the system comprises a sound
and/or video hub, wherein the sound and/or video hub allows any
member of the plurality of the members to play a song and/or a
video and simultaneously allows the plurality of members to listen
and/or watch the song and/or the video played.
12. The system of claim 6, wherein the system comprises audio
and/or video synopsis of the collaborative interactive session for
the plurality of members using a sound and image-processing
technology that creates a summary of an original full-length audio
and/or video.
13. The system of claim 6, wherein system is configured to
determine a mental health of a patient by analyzing one or more of
audio, video, textual and location data of the patient, and
evaluating the data in a standardized model.
14. A tangible non-transitory computer readable medium comprising
computer executable instructions executable by one or more
processors for establishing the collaborative platform of claim 1,
comprising performing a collaborative interactive session for a
plurality of members, and analyzing affect and cognitive features
of the plurality of members.
15. The medium of claim 14, wherein the plurality of members
interacts from different human interaction platforms.
16. The medium of claim 14, further comprising computer executable
instructions executable by one or more processors for displaying of
targeted advertisements or notifications based on the context of
the interactive collaborative sessions.
17. The medium claim 14, wherein the executable instructions
comprise instruction for determining a mental health of a patient
by analyzing one or more of audio, video, textual and location data
of the patient, and evaluating the data in a standardized model.
Description
CROSS-REFERENCE TO RELATED APPLICATIONS
[0001] This application is a continuation of U.S. patent
application Ser. No. 13/668,337, filed Nov. 5, 2012, which claims
priority to U.S. Provisional Application No. 61/720,405, filed on
Oct. 31, 2012, entitled "The Next Generation of Virtual Live
Education"; U.S. Provisional Application No. 61/719,980, filed Oct.
30, 2012, entitled "Online Social Interaction, Education, and
Health Care by Analysing Affect and Cognitive Features"; U.S.
Provisional Application No. 61/625,949, filed Apr. 18, 2012,
entitled "SWAP The Next Generation of Virtual Communication
Platform," and U.S. Provisional Application No. 61/556,205, filed
on Nov. 5, 2011, entitled "SWAP: FUTURE OF VIDEO CHATTING," which
are incorporated herein in their entirety by reference.
[0002] All U.S. patents and publications listed in this application
are incorporated herein in their entirety by reference. This
application is also related to the U.S. patents and publications
listed in Appendix 1. These U.S. patents and publications listed in
Appendix 1 are incorporated herein in their entirety by
reference.
BACKGROUND
[0003] According to International Data Corporation (IDC), a global
provider of market intelligence, video communications is one of the
most promising industries with the potential to create a market of
at least 150 million people in America alone in the next five
years.
[0004] Certain video communication platforms for groups of
individuals to create and share information, interact with each
other through the software and generally use the software to
achieve an individual or group objective are currently available.
Generally these systems store the collaboration for future
reference and further discussion or collaboration. However, these
systems have several limitations that have been addressed herein.
Also, novel solutions for these limitations are provided
herein.
SUMMARY
[0005] The embodiments herein relate to a method of establishing a
collaborative platform comprising performing a collaborative
interactive session for a plurality of members, and analysing
affect and cognitive features of some or all of the plurality of
members.
[0006] In one embodiment, some or all of the plurality of members
from different human interaction platforms interact via the
collaborative platform,
[0007] One embodiment can further comprise displaying of targeted
advertisements or notifications based on the context of the
interactive collaborative session.
[0008] One embodiment can further comprise measuring effectiveness
of the displaying of targeted advertisements or notifications.
[0009] One embodiment can further comprise integrating an
application or a device within the collaborative interactive
session.
[0010] Another embodiment relates to a computer implemented system
comprising: a storage medium configured to store a collaborative
interactive session data; and a processor configured to perform a
collaborative interactive session for a plurality of members,
wherein the system analyses affect and cognitive features of some
or all of the plurality of members.
[0011] In one embodiment, some or all of the plurality of members
from different human interaction platforms interact via the
collaborative interactive session, wherein the different human
interactions platforms comprise social media platforms.
[0012] In one embodiment, the system is further configured to
display targeted advertisements or notifications based on the
context of the interactive collaborative sessions.
[0013] In one embodiment, the system is further configured to
measure effectiveness of the displaying of targeted advertisements
or notifications.
[0014] In one embodiment, the system is further configured to
integrate an application or a device within the collaborative
interactive session.
[0015] In one embodiment, the system comprises a sound and/or video
hub, wherein the sound and/or video hub allows any member of the
plurality of the members to play a song and/or a video and
simultaneously allows some or all of the plurality of members to
listen and/or watch the song and/or the video played.
[0016] In one embodiment, the system comprises audio and/or video
synopsis of the collaborative interactive session for the plurality
of members using a sound and image-processing technology that
creates a summary of an original full-length audio and/or
video.
[0017] Another embodiment relates to a tangible non-transitory
computer readable medium comprising computer executable
instructions executable by one or more processors for establishing
a collaborative platform comprising performing a collaborative
interactive session for a plurality of members, and analyzing
affect and cognitive features of some or all of the plurality of
members.
[0018] In one embodiment, some or all of the plurality of members
interact from different human interaction platforms.
[0019] One embodiment could further comprise computer executable
instructions executable by one or more processors for displaying of
targeted advertisements or notifications based on the context of
the interactive collaborative sessions.
[0020] The foregoing summary is illustrative only and is not
intended to be in any way limiting. In addition to the illustrative
aspects, embodiments, and features described above, further
aspects, embodiments, and features can become apparent by reference
to the drawings and the following detailed description.
BRIEF DESCRIPTION OF THE FIGURES
[0021] FIG. 1 shows an embodiment of data passing through the SWAP
platform, wherein data is acquired and the multimedia segmented and
analysed.
[0022] FIG. 2 shows an embodiment of chatting threads.
[0023] FIG. 3 shows an embodiment of profile appearance.
[0024] FIG. 4 shows an embodiment of the analysis of data through
SWAP for `close friends`.
[0025] FIG. 5 shows an embodiment of e-learning showing an user
interface for studying from a video lecture.
[0026] FIG. 6 shows an embodiment of a virtual classroom with
e-material used as learning medium, based on study of eye movement,
pupillary dilation and facial study.
[0027] FIG. 7 shows an embodiment of a user interface.
[0028] FIG. 8 shows an embodiment of a collated version of the user
interface.
[0029] FIG. 9 shows an embodiment of how insurance or health care
companies will acquire date through the cell phone of a client.
[0030] FIG. 10 shows an embodiment of how the media acquired by
SWAP is going to be analyzed.
[0031] FIG. 11 shows an embodiment of a non-invasive patient
tracking method.
[0032] FIG. 12 shows a flow diagram that delineates possible
tracking mechanisms.
DETAILED DESCRIPTION
[0033] Large amount of online media that is transferred is merged
providing convenience to user. This data is analysed to find out
affect and cognitive state. Utilising this data a new form of
social interaction platform is developed which will incorporate
many features of real human interaction.
[0034] The term "affect" refers to the experience of feeling or
emotion. Affect is a part of the process of an organism's
interaction with stimuli. The word also includes affecting display,
which is a facial, vocal, or gestural behavior that serves as an
indicator of affect.
[0035] The term "cognitive state" refers to the state of exploring
internal mental processes, for example, to study how people
perceive, remember, think, speak, and solve problems.
[0036] SWAP is the acronym of an embodiment of a virtual
communication platform system described herein. SWAP and a virtual
communication platform system are used synonymously in this
application.
[0037] Embodiments herein relate to SWAP, which can be a web-based
application that serves as a multi-dimensional platform for
peer-to-peer communication. Current video communication services
such as Skype only provide basic face-to-face contact pathways--the
interaction is limited to text, audio, and video. SWAP integrates
collaboration with communication. It streamlines the base services
of peer-to-peer text, audio and video communication with
interaction on various collaborative platforms as well as with
individual web-based activity. SWAP can incorporate existing
streams of social media. SWAP strives to be the global leader in
providing a unified collaboration platform using Internet
communication media while enhancing the capabilities of virtual
interaction of people from all walks of life. SWAP can provide
young adults with a video communications application that
integrates multiple streams of online media with virtual
interaction. SWAP can provide a unified platform that allows users
of any social media service, such as FACEBOOK.RTM. or GOOGLE+.RTM.,
to interact on, removing the fragmentation within social media
communication. This platform also combines text, audio, and video
communication with collaboration in the areas of academia, music,
and recreational activities such as gaming, extending the
capabilities of current virtual communication.
[0038] This application can be organized into several spheres of
interaction known as "globes". Each globe can provide a base
interaction for multiple users to collaborate. Our application can
integrate these collaboration platforms with a video feed to
enhance overall virtual interaction.
[0039] The data passing through the SWAP platform will be acquired
and the multimedia will be segmented and analysed. This can be seen
in FIG. 1. FIG. 1 depicts the SWAP platform that solves the problem
of fragmentation and provides a seamless connection between users
from separate platforms. These two separate platforms are
integrated by SWAP. Interactions between User 1 and User 2 are then
sent for analysis. These interactions are used in the Swap+
Profile, Elearning, and SWAP Project.
[0040] The derived information from analysis such as user emotion
and mental states will be utilised in functioning of 3 major SWAP
features--
[0041] 1. Profiles (SWAP+)
[0042] 2. Targeted Advertisement
[0043] 3. Smart ELearning (addition to the chalkboard and virtual
classroom globe)
SWAP+ Profiles
[0044] The way most social networking sites function, they mainly
act as a great platform for data storage, sharing and
communication. But they are all a far cry from true social
interaction simulation in other words in no way are these anywhere
near how we interact in society. Thus the profiles of SWAP+ will be
a system which will be much closer to how we remember people,
conversations and moreover how we forget. The large amount of data
that get passed through the SWAP platform will be analyzed and this
data will be used to shape the SWAP+ profiles. The way other
people's SWAP+ profiles will appear to us. In this area we try to
mimic the way in which we remember people. The profile's emotion
feel will be the general emotion that we generally exhibit when we
communicate that with that person through any form of media (video,
text or speech) (obtained from analyzed data from conversations
taking place). Keeping in trend with how we remember people in
reality, since how a person is seen by is strongly shaped with
event and experiences we share with that person. The profile of the
person will bear events, having strong emotions behind them. Any
sort media--like text, speech, video or pictures. Texts can be
presented simply as they are, videos will we presented like
snapshots with the option to be played by the user.
[0045] The SWAP+ profile can include:
[0046] 1. Chatting threads (as depicted by FIG. 2)
[0047] 2. Profile appearance (as depicted by FIG. 3)
[0048] 3. Close friends (as depicted by FIG. 4)
1. Chatting Threads
[0049] The basic flaw which makes social interactions unrealistic
is that every bit of data is remembered, unlike the case in
real-life daily interactions. To replicate this communications that
will be happening through SWAP+ will follow a similar pattern. The
comments of the thread will slow start to deteriorate i.e. fade
away. The period after which the part of the thread is completely
forgotten will be a sort of threshold time, which will be close to
average human being time for memory retaining. Memories having high
cognitive strain or emotion attached will have much higher
threshold time.
[0050] In FIG. 2, the comments of the thread will slow start to
deteriorate i.e. fade away. The period after which the part of the
thread is completely forgotten will be a sort of threshold time,
which will be close to average human being time for memory
retaining. Memories having high cognitive strain or emotion
attached will have much higher threshold time. This example shows a
conversation between two friends discussing "last night's party."
The initial conversation contains low emotionally attached or
insignificant conversation (e.g. "Hey!" "What happened?" "Yeah?").
In the decayed conversation, however such aspects of the
conversation are decayed into visually smaller bubbles. The larger
bubbles include phrases associated with high cognitive strain or
emotion attached. In this example, one friend tells the other "I
told mom about how I lost her gold necklace." This phrase is the
largest bubbles, assuming that the friend was experiencing
significant emotion--including perhaps anxiety, fear, etc.
2. Profile Appearance
[0051] In FIG. 3, the profile of SWAP+ will be dynamic in nature
constantly changing reflecting the mental state of the user. The
real-time mental state will be determined from the various analysis
methods, which will be applied on the data passing through SWAP.
Under a state of extreme emotion such as depression the user
profile will be able to reflect this state. This will allow for
other people to be notified of the user's emotional state and hence
help him get back normalcy through communication. Through analysis
`close friends` can also be identified who under the
above-mentioned situation will be notified. In this example, we see
Abhishek Biswas' profile as seen by his girlfriend. Note: this
profile is individualized. His girlfriend can see all the important
conversations between them (as "decayed conversation" feature.)
These conversations include highly emotional both positive and
negative phrases. Also, highly emotional paused scenes from videos
will appear as well as pictures that have been discussed in
emotional conversations.
3. Close Friends
[0052] FIG. 4 demonstrates the analysis of data through SWAP for
`close friends`. The analysis will allow the application to
identify people with whom the user has discussions of high
emotional content. A database of sorts can be created which will
store people with whom the user has discussion of high emotional
content such as high positive emotion content, high negative
emotion content, people with whom through communication emotion
changes from negative to positive. Also, people with whom there
isn't communication of high emotion content, but volume and
frequency of communication is very high, these people will also be
identified as `close friends`.
[0053] Whenever the user is in a state of emotional extreme then
the user's profile will be highlighted in the homepages of the
`close friends`. In this example, the friend whose profile shows
high levels of distress is the largest. The user can visually
identify this friend and try to help her. The second largest
picture is also a friend who is visually distressed (which is seen
through emotions detected on his profile) and is therefore seen as
a large image. The third largest image is the user's girlfriend's
profile. Although her profile does not show high emotional context,
her profile image is highlighted because of the high volume and
frequency of communication.
Elearning
[0054] In virtual classroom or chalkboard feature the user may be
required to go through leaning material or modules and solve
problems. Based on observation of Pupil dilation the cognitive load
on user's mind can be found out. If the user is under high
cognitive stress for prolonged period it is indicative that the
user is unable to make progress with current material or problem.
Hence more comprehensive material may be provided and in case
problems a hint or a different problem may be provided. Similarly
the pupil study may also indicate the course and problems may not
cause appreciable cognitive strain so in this case a course which
is less comprehensive and problems of higher difficulty may be
presented. The SWAP feature will allow people from different video
communication platforms to join into a virtual classroom. This
virtual class room will allow for multiple people to join at same
time the course being taught will customized for each individual
user. Thus student gets all the benefits of study in a classroom
such discussion, debating, interactive doubt clearance, observing
point of view of peers. At the same time the course is modified as
peer the learning capacity and mental level of each individual
student. So as the all students join the virtual classroom they all
start out with the same course material and as they carry forward
with class, constantly each student cognitive load level,
attention, stress is being monitored. And based on this data
material is modified that will enable maximum learning will be
provided. Apart from pupillary dilation and video analysis of face,
eye tracking will allowing monitoring the movement of the eyes
hence it will be possible to see whether that user is being able to
focus on the material. Using eye tracking technology, we can find
the place where the user is looking at and pattern recognition can
be utilized to find whether the material being presented is being
read or not for example regularized movement of eyes indicate that
the user is following the material presented and whereas wandering
and random movement of eyes are indicative that the material is not
being followed.
[0055] The virtual classroom element of SWAP will have advanced
tools to simulate real class room like environment. The nature
learning may be of 2 types; video lecture and course material.
[0056] FIG. 5 shows the user interface for studying from video
lecture. The following features are present: a notepad where the
user can take rough notes, inventory of study aids (like
calculator), formula manual (for the course being studied), and
access to all rough notes. The work area contains questions and
problems that will be asked to the user as each sub segment of the
video lecture is completed for those of who finish the problems
quickly more problems will be asked and the next sub segment may
start only after the minimum number of questions has been completed
by everyone. The user will be able to communicate with his other
peers and ask them for help or doubt clearance (similar to real
class rooms). The feature will also be provided that allows for
person who is communicating with user to share his work sheet and
help in solving and understanding the problem. As can be seen in
this example, the lecture is synchronized with the notes in the
formula manual being displayed. Also, based on eye movement,
pupillary dilation and facial study of other peers, the student (or
teacher) can detect the amount of difficulty or ease his/her peers
is having with the class and the problems.
[0057] FIG. 6 shows a virtual classroom with e material used as
learning medium, based on study of eye movement, pupillary dilation
and facial study. The material will be constantly modified and
since all peers will be present constant discussion will also be
taking place.
[0058] If it is observed that the user wasn't taking in the course
then pop up questions will be presented on the work area, to check
the users understanding hence allow for optimised learning.
[0059] Also, based on eye movement, pupillary dilation and facial
study of other peers, the student can detect the amount of
difficulty or ease his/her peers is having with the class and the
problems. Areas that seem to be confusing for the student will be
noted down and at the end of each study session these areas will be
reviewed.
SWAP Projects
[0060] FIG. 7 shows an embodiment of a user interface. For example,
another feature that will be present along with the education globe
is a single sheet that can be shared across an entire group. All
members of the group can make modifications to the sheet
simultaneously. All editing and word processing features will be
made available. This will allow for rapid completion of project
with different parts (e.g. one person may be drawing up some part
while others may be writing) being done by different people. In
FIG. 7, for example, Linda, Jenny, and Robert can all see each
other's videos. The "Toolbar" includes all possible software
devices (e.g., tables, different languages, presentation tools,
graphical tools, etc.) In this image, one of the users is able to
see what he/she has entered into the project.
[0061] Since all progress being made is constantly visible to all
the users working on it, a seamless integration will be possible.
In fact different people can comment and suggest changes to some or
more parts being done by someone else. Constant discussion and
visibility amongst the different team members will also be
facilitated through audio and videoconference, which will run in
parallel with the SWAP Project feature. This will have huge utility
in corporate sector, which generally have members working on single
project scattered all over the globe.
[0062] FIG. 8 shows an embodiment of a collated version of the user
interface. On a single screen, smaller images of various different
types of software applications can be presented. Also each user's
specific part is labelled automatically with their name. Thus,
users are able to see the different segments of the project that
are being completed by other users.
Targeted Advertisements
[0063] Advertisement will be presented to users based on [0064] a.
Keyword matching [0065] b. Based on knowledge of user's real-time
emotional state. [0066] c. Geographic location and movement pattern
(for people using mobile access medium like cell phones or
tablets)
[0067] The advertisements that will be presented will be guided
based on the content of the conversation, the mood of the user and
the feature that of SWAP that is being used.
[0068] For example people who show high level of cognitive stress
may be suggested stress-relaxing medicine, names of specialists and
people. People showing emotional extremes like extreme depression
may be suggested holiday destinations and retreats, or books.
[0069] For mobile users the geographical location, path and
movement pattern of the user will be taken into account to provide
location based targeted advertisement where product that might
appeal to user (predicted by taking into factors like nature of
conversation, or media being observed, mood of the user and
geographical position). This will enable advertisement agencies to
provide extremely specific advertisement.
Healthcare (Remote Diagnosis)
[0070] Advanced application can be developed which will collect
data generated from cell phones and transfer these to service
provider who will analyse the data and transfer it to the
healthcare agencies who can then provide diagnosis on basis of the
data provided.
[0071] Advancement in cloud computing enables us to utilise same
apps from different computing devices like tablets, computers,
laptops and cell phones. The apps thus developed will not device or
platform specific but will only be user specific, they will have an
inventory of data mining mechanisms and depending on the device
being used select mechanisms will be used.
[0072] Combination of data collected from the multiple sources will
used to determine lifestyle of the person and this can be used by
healthcare and insurance industries. This cycle is depicted in FIG.
9, which shows an embodiment of how insurance or health care
companies will acquire date through the cell phone of a client. The
cellular data will go through a telecom operator, through a
3.sup.rd party service provider, who will collect and analyze the
data and return it back to the company.
[0073] 3rd party provider can collect this data only after approval
from the individual who owns the cellular device over a set period
of time. The data can be used by the individual for personal usage
or along with his/her doctor for health analysis. For example, an
individual who is fighting with obesity can have his/her cellular
data tracked for one month. After analysis of this data, the doctor
and patient (e.g., an obese individual) can work together to target
some of the problems that the patient. On the other hand, health
insurance companies can use this data after approval from the
potential customer to determine how healthily he/she is living. If
the behavioural choices, emotions, and other everyday actions of
the customer seem to promote healthy lifestyle, the insurance
company can give discounted rates to such a costumer. There are
three methods by which current day smart phones can determine the
lifestyle, behaviour, or emotions of a person. Time and location,
the audio vector of the cellular device, and typing characteristics
can be used to analyse a person's health. This data will be
collected over a span of time.
[0074] Lifestyle data will include:
[0075] 1. Location information
[0076] 2. Driving information and movement information
[0077] 3. His affective state and average cognitive state
[0078] 4. Habitual information--diet, drinking, etc.
[0079] 5. Real-time information about physical health
[0080] The span of time and monitoring parameters will be
determined jointly by user and concerned agency.
1. Location Information:
[0081] The geographical location of a person can give a general
idea of the person's life style and personality. Information like
movement over different non-urban terrain is indicative of an
adventurous lifestyle. Also information like the places the person
visits will highlight many of the persons traits e.g., location
data showing that one visits McDonald's everyday indicates that the
individual does not have a healthy lifestyle, compared to an
individual who visits the gym on a daily basis. After large enough
samples of data are collected, a movement map of the individual can
be created that shows frequencies of visits to certain locations
within a certain range. Using a pattern identification algorithm,
doctors or life insurance agencies can more easily analyse location
data of an individual and correlate this to his/her lifestyle.
2. Driving Information and Movement Information:
[0082] Velocity and acceleration analysis can be done by the GPS on
the phone to determine whether or not the individual is a rash
driver. Information about speed limits on a majority of roads is
present on the maps that are on smart phones. It can be understood
that an individual is driving if they are on a road that is
traversed upon by vehicles. Usually, GPS tracking provides an
accuracy of within 50 meters. So, the speed of a person can be
determined by dividing each 50-meter path covered by the time
required by the individual to traverse that distance. It will be
noted that a person is walking, not driving, on such a road if the
speed is significantly below that of the speed limit (like below 10
km/s) for an extended period of time. Even this information is
vital, as it informs that the individual is walking on a road that
is meant for vehicles, which in itself is an unsafe behaviour. This
behaviour will not be confused with cars that are just stuck in
traffic, because traffic patterns are now being updated constantly
to smart phones, and data about the location and time of the
traffic can easily be collected. After confirming that the
individual is driving on the road, one can compare the speed of
his/her vehicle with the speed to determine whether or not the
person is speeding. Even if the individual whose data is being
taken down is not the driver, it is important to know if the
individual is at risk by being in the same vehicle as a person who
is speeding. In addition, if the average velocities recorded in
each 50 meter block are fluctuating highly, and the time taken to
cover one 50 meter stretch is significantly different than the time
taken to cover another, one can see that the driving is "stopping
and going" too frequently. An accumulation of data about velocity
can easily be translated into acceleration analysis, where the
rashness of the driver with sudden accelerations can be
determined.
3. The Affective and Cognitive State:
[0083] The user emotional and cognitive data will have obtained
from all communications taking place in form texting, video chat
and audio chat from devices like smart phones, tablets computers or
laptops. Since the functioning of various features of SWAP like
profile+ and virtual classrooms is heavily of dependent on user
emotion and cognitive state the apps can gather data from these
features to observe emotional and cognitive states of the user
during the period of observation. These data can be combined with
location data (considering the fact that the user is constantly
carrying his smart phone) to affect map of the person. The affect
map will show which emotions and mental state correspond to
specific locations of the individual.
4. Habitual information:
[0084] Various apps and detection mechanisms can be utilised to
determine various habits of the user like eating habits, drinking
habit, smoking habit, etc. Apps like MEALSAPP.RTM., etc. can be
detected by the advanced apps of SWAP and used to detect traits of
the user.
5. Physical Health information:
[0085] Smart phones have pedometers installed in them and also have
the capacity to find a person's pulse. All these features can be
used by advanced SWAP apps to give a person's physical health
status which can be further combined with time and location
information supplement the above-mentioned data.
[0086] From this network, an emotional map can also be constructed
that shows which emotions correspond to specific locations of the
individual. This location tracking combined with the audio vector
and typing analysis can indicate which locations the individual
should continue going to boost happiness and which locations should
be avoided, as they may be correlated to stress, anger, sorrow,
etc.
Emotion Analysis
[0087] The large amount of data that will be passing through SWAP
will be analysed in following ways:
[0088] 1. Video Analysis
[0089] 2. Speech Analysis
[0090] 3. Typing analysis
[0091] FIG. 10 illustrates an embodiment of how the media acquired
by SWAP is going to be analyzed. The data can be organized into
three segments: Video, Text, and Audio. Pupillary dilation analysis
and facial feature analysis can be taken from the video data
analysis. From textual data, keywords, knowledge-based artificial
neural networks, typing speed, pressure, contextual clue and error
analyses can be done. From audio data, features can be extracted
and analyzed. These can be used to determine emotion.
[0092] 1. Video Analysis [0093] a. Facial emotion recognition
[0094] The emotion of user is recognized by tracking the movements
of some fixed points of the face like the corners of eyes, mouth
boundary, etc. The amount of movement of these points in various
frames of the video are constantly monitored and the data thus
generated is fed in various classifiers like Bayesian Networks,
Decision Trees etc. from which we find the emotion of the user.
[0095] b. Pupillary Dilation [0096] Dilation of pupils is common
phenomena. The causes for dilation of pupils are: [0097] 1. Mental
stress (cognitive load). [0098] 2. Emotion [0099] 3. Light
stimulus
[0100] Our pupils tend to dilate in different emotional situation.
Studies conducted have shown that with increase in arousal level
the diameter of out pupils increase. Also valance causes our pupils
to dilate. But the amount of dilation caused for positive and
negative emotion has been found out to be the same. This issue may
be resolved with further study in this area--analyzing the rate of
dilation and dilation period and also the amount and rate of
dilation under combination of different stimuli. Also while
measuring pupil dilation, the dilation caused due other stimuli
like light have 2 either ignored or factored out (more study is
required in this area). Pupillary dilation is a complete
involuntary reflex and hence there no change for us to consciously
control it. (This is possible in case facial emotion recognition.)
Hence no emotion faking is possible. A distinct difference is
apparent for male and female users. So, gender classification can
be done easily through study of pupil dilation pattern.
2. Speech Analysis
[0101] To find out emotion from speech the basic idea is to study
the way the voice box functions while producing speech under
different emotional states. Depending upon how it functions
variations in wave form appear. By extracting the various features
of the waveform from which these variations can be detected and
putting these (certain combinations of features) into various soft
computing models the emotion can be predicted.
[0102] Data extracted from an audio vector can be used to determine
one's emotional state. The volume and pitch of the speaker can be
found without actually recording what the speaker is saying,
avoiding any invasion of privacy. The content of the conversation
is immaterial to the 3.sup.rd parties, since only the tonal nature
(loudness and frequency) of the individual is being analyzed.
[0103] To find emotion from speech first we extract various
components of speech, which carry data with respect to emotion.
These components are energy, pitch, cross sectional area of vocal
tract tube, formant, speech rate and spectrum features and spectral
features like linear prediction coefficients (LPC), linear
prediction cepstrum coefficients (LPCC), Mel frequency cepstrum
coefficients (MFCCs) and its first derivative and log-frequency
power coefficients (LFPCs). All these components are extracted from
the original speech waveform using various mathematical and
statistical techniques. The features can be extracted utilizing
various combinations of the features. These acoustic features are
used to find out emotions through various classifiers.
[0104] Methods that classify emotions from prosody contours are
neural networks, multi-channel hidden Markov model, mixture of
hidden Markov models these give prediction from the temporal
information of speech
[0105] Methods which classify emotions from statics of prosody
contours support vector machines, k-nearest neighbours, Bayes
classifiers using pdf (probability distribution functions)
generated by Parzen windows, Bayes classifier using one Gaussian
pdf, Bayes classifier using mixture of Gaussian pdfs.
[0106] Hence from the above mentioned soft computing techniques we
find the emotion of a person. From this his type of collection over
a large span of time, general emotional status can be determined
via the audio vector.
[0107] Data extracted from an audio vector can be used to determine
one's emotional state. The volume and pitch of the speaker can be
found without actually recording what the speaker is saying,
avoiding any invasion of privacy. The content of the conversation
is immaterial to the 3.sup.rd parties, since only the tonal nature
(loudness and frequency) of the individual is being analysed.
3. Typing Analysis
[0108] We will utilize the following methods to find emotion of the
user from the text that he types. All the methods will be working
in parallel. [0109] 1. Finding emotional keywords in textual data
and deriving the emotion of the sentence from that. [0110] 2.
Finding emotion from sentences, lacking emotion key words using
Knowledge Based Artificial Neural Networks. [0111] 3. By analyzing
the typing speed. The various features of typing that we study are
time lag between consecutive keystrokes [0112] 4. Error level.
(Number of times corrections are made in the sentences). [0113] 5.
Pressure Analysis--the pressure sequence various features extracted
like mean, standard deviation, maximum and minimum energy
difference, the positive energy center (PEC) and the negative
energy center (NEC). PEC and NEC are calculated from mean and
standard deviation after normalization). [0114] 6. Contextual cue
analysis weather, lighting, temperature, humidity, noise level and
shaking of the phone
[0115] The various features of typing that we study are time lag
between consecutive keystrokes, number of times back space is used,
typing speed and pressure put behind each keystroke, for example,
from the pressure sequence various features extracted like mean,
standard deviation, maximum and minimum energy difference, the
positive energy centre (PEC) and the negative energy centre (NEC).
PEC and NEC are calculated from mean and standard deviation after
normalisation). Apart from these various contextual cues are also
taken into account like weather, lighting, temperature, humidity,
noise level and shaking of the phone, and the frequency of certain
characters, words, or expressions can be used to determine emotion.
The above mentioned sets of features are fed into various soft
computing models (like support vector machines, Artificial neural
networks, Bayesian networks, etc.), these generate probability
towards a particular emotional state individually for each set of
features. Also, since in most cases the outcome will be towards the
same emotion from computations on each feature set hence fusion
methods can be used to compute the over all probability of having
that particular emotion by combining the individual results.
Towards Development of a Model for Emotion Detection from Typing
Analysis
[0116] First we find out features of typing which is exhibited by
most people and features of these patterns which detect emotions.
We now develop various soft computing models which allow for the
detection of a particular emotion from the typing pattern. To see
the efficiency and functionality of these models we conduct sample
studies where a software is downloaded by the people whose typing
pattern will be analysed. Apart from the typing pattern detection
another detection method will also be there to measure the
emotional state at the time of typing. These 2 methods will work in
parallel and the emotion detected by latter method will be taken as
reference and later during analysis it will be seen whether the
emotion predicted by the former method matches with the
reference.
[0117] In the latter method the peoples' emotional valence will be
detected by study of their facial muscles which can be done by use
of a simple web-cam (generally available with their computer or
laptop) and arousal will be detected by measuring the galvanic
conductivity of skin measured with wristband with this capability
(already a commercial product manufactured by a company called
AFFECTIVA.RTM.).
[0118] The above-mentioned method departs away from way experiments
have been done on typing analysis recently. In these experiments
the candidates who's pattern will be analysed are given the
software which analyses the typing pattern but reference emotion is
found out through questionnaires that enquire about the emotion of
the person before he starts to type.
[0119] Again, this will not be a privacy issue because these third
parties will not access full texts. They will just automatically
search through them for the frequency of specific words or
expressions that may correlate to the individual's emotions. These
data will not just be collected once, but over a long span of time.
As a result, the overall emotional and behavioural state of
individual will be determined. So, a person typing very fast on a
shaking phone, with high pressure under the keys, and using a high
frequency of unpleasant words used in his/her texts can reveal
anger or stress. However, if data that points to this behaviour is
only collected once or twice in a span of a month, it will not be
regarded as very important, as everyone has some infrequent
expressions of anger or stress. However, if a majority of typing
data is like this, a doctor of insure company can infer that the
individual is constantly angry or stressed out, which is not good
for health.
Mental Health Tracker
[0120] Currently 1 in 4 Americans have a mental disorder. It is
becoming increasingly important to identify mental disorders at
younger age, when symptoms are still slight. It is thus essential
for primary care physicians in addition to psychiatrists to be able
to recognize mental disorders.
[0121] In an embodiment, the DSM IV-TR (Diagnostic and Statistical
Manual for Mental Disorders) and DSM IV-PC (Diagnostic and
Statistical Manual for Primary Care) version, which are the manuals
used by doctors to determine both the presence and category of
mental disorder, could be included in as part of a computerized
algorithm to help doctors for patient tracking. The DSM IV-PC
(meant for primary care physicians, who are not specialized in
mental disorders) has organized symptoms that create a diagnostic
algorithm. This manual is concise and fully compatible with the
wider used DSM IV-TR, which is used by psychiatrics.
[0122] Primary care physicians (PCP) have made many initial
diagnoses of mental disorders. However, many diagnoses remain
undetected, as PCPs generally only have check-ups with patients one
or twice a year, and mental disorders, at first may be difficult to
observe, as there are no standardized tests for mental disorders.
Due to the difficulty in diagnosing a mental disorder within the
limited face-to-face patient-doctor interaction, it can be
extremely helpful for doctors to use a non-invasive patient
tracking method of an embodiment as shown in FIG. 11, which shows
the main aspects of SWAP that can be used to create a profile of
the patient that can then be analyzed by the algorithm of the
DSM-IV and by doctors.
[0123] Doctors can track their patients using methods detailed in
other examples of our patent. FIG. 12 shows a flow diagram that
delineates possible tracking mechanisms. FIG. 12 shows that the
proposed tracker can use video data, time and location analysis,
typing analysis, and audio data in order to understand the
patient's emotional state. Over a week or month long analysis, this
tracker will then use an algorithm from the DSM-IV in order to
identify an initial mental diagnosis. With the use of the
guidelines in the DSM-IV-PC, the algorithms created by the manual
can be used along with our tracking system to provide a primary
initial screening for patients for detection and type of mental
disorder. Thus, SWAP's Mental Health Tracker can help a physician
better understand his patient's needs.
APPENDIX 1
[0124] The U.S. patents and publications listed below are hereby
incorporated herein by reference in their entirety. U.S. Pat. No.
8,102,406; Issue date: Jan. 24, 2102; Method and system for
producing a video synopsis U.S. Pat. No. 8,073,839; Issue date:
Dec. 6, 2011; System and method of peer to peer searching, sharing,
social networking and communication in one or more networks U.S.
Pat. No. 7,523,163; Issue date: Apr. 21, 2009; Distributed network
system architecture for collaborative computing U.S. Pat. No.
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and playback of collaborative web browsing session U.S. Pat. No.
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transmission over network protocols U.S. Pat. No. 6,567,813; Issue
date: May 20, 2003; Quality of service maintenance for distributed
collaborative computing Publication number: US 2011/0258125; Filing
date: Apr. 14, 2011; Collaborative social event planning and
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16, 2011 Social media platform for simulating a live experience
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Sports collaboration and communication platform Publication number:
US 2010/0299334; Filing date: Sep. 8, 2009; Computer implemented
system and method for providing a community and collaboration
platform around knowledge transfer, expertise, innovation, tangible
assets, intangible assets and information assets Publication
number: US 2010/0332616; Filing date: Aug. 31, 2009; Web guide
Publication number: US 2010/0262550; Filing date: Apr. 8, 2009;
Inter-corporate collaboration overlay solution for professional
social networks Publication number: US 2009/0094039; Filing date:
Oct. 4, 2007; Collaborative production of rich media content
Publication number: US 2008/0297588; Filing date: May 31, 2007,
Managing scene transitions for video communication Publication
number: US 2005/0198141; Filing date: Feb. 4, 2005; Secure
communications system for collaborative computing Publication
number: US 2003/0167304; Filing date: Dec. 29, 2000; Distributed
meeting management Publication number: US 2003/0164853; Filing
date: Dec. 29, 2000; Distributed document sharing
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