U.S. patent application number 16/244626 was filed with the patent office on 2019-05-16 for system and method for processing information and mentoring people.
The applicant listed for this patent is David A. DILL. Invention is credited to David A. DILL.
Application Number | 20190147377 16/244626 |
Document ID | / |
Family ID | 61560063 |
Filed Date | 2019-05-16 |
United States Patent
Application |
20190147377 |
Kind Code |
A1 |
DILL; David A. |
May 16, 2019 |
SYSTEM AND METHOD FOR PROCESSING INFORMATION AND MENTORING
PEOPLE
Abstract
A method for processing information for mentoring service to
at-risk people is described. Also describes is a system for
implementing the method and a tangible computer readable medium for
storing instructions that, when executed by computer processor,
causes the computer processor to process information according to
the method of the present application.
Inventors: |
DILL; David A.; (Newtown,
PA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
DILL; David A. |
Newtown |
PA |
US |
|
|
Family ID: |
61560063 |
Appl. No.: |
16/244626 |
Filed: |
January 10, 2019 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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15691253 |
Aug 30, 2017 |
10217070 |
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16244626 |
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15623206 |
Jun 14, 2017 |
10133998 |
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15691253 |
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15373869 |
Dec 9, 2016 |
10068194 |
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15623206 |
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15266384 |
Sep 15, 2016 |
10229378 |
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15373869 |
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15337799 |
Oct 28, 2016 |
10019688 |
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15623206 |
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15266384 |
Sep 15, 2016 |
10229378 |
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15337799 |
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Current U.S.
Class: |
705/7.28 ;
705/7.38 |
Current CPC
Class: |
G06Q 10/0635 20130101;
G06Q 30/0208 20130101 |
International
Class: |
G06Q 10/06 20060101
G06Q010/06; G06Q 30/02 20060101 G06Q030/02 |
Claims
1. A method for processing information for providing mentoring
services to at-risk people, comprising the steps of: receiving, via
a user interface of an application executing on one or more
computer processors, a personal profile concerning an at-risk
subject, wherein the personal profile comprises personal data, a
risk profile comprises a plurality of risk factors and a plurality
of mentee matching tags; assigning, via the one or more computer
processors, a risk point value to each of the plurality of risk
factors based on severity level of the subject's risk factors and a
risk point matrix stored on a memory device accessible by the one
or more computer processors, wherein the risk point matrix is
generated using ontology related artificial intelligence and a risk
database reflecting risks of other subjects compared to their later
successes or failures, and estimated lifetime costs to society;
determining, via the one or more computer processors, a total risk
point value of the at-risk subject via the one or more computer
processors; accepting the at-risk subject as a mentee candidate, if
the total risk point value satisfies a pre-determined threshold
value; searching, via the one or more computer processors, a mentor
candidate database comprising a plurality of mentor profiles,
wherein each mentor profile comprises personal data, mentoring
experience and a plurality of mentor matching tags; assigning, via
the one or more computer processors, at least one mentor candidate
to the mentee candidate, wherein the at least one mentor candidate
is selected based on a match between the mentee matching tags and
the mentor matching tags, where in the match is performed using a
matching algorithm; submitting the at least one assigned mentor
candidate to an oversight board for approval; receiving, via the
user interface of the application, the at-risk subject's progress
report after the establishment of a mentor-mentee relationship;
comparing, via the one or more computer processors, the at-risk
subject's progress to at-risk individual success odds or ex-convict
success odds stored on a memory device accessible by the one or
more computer processors; obtaining, via the one or more computer
processors, (1) the at-risk subject's income tax records from
relevant governmental agencies, (2) the at-risk subject's
retirement plan contribution information, and/or education plan
contribution information from relevant financial institutions or
(3) a forecast of the at-risk subject's future income tax and
retirement plan and/or education plan contribution if the subject
is successfully mentored; determining, via the one or more computer
processors, a financial incentive to the mentor based on result of
the comparing step, the at-risk subject's income tax records, the
at-risk subject's retirement plan contribution information and/or
education plan contribution information, or the forecast of the
at-risk subject's future income tax and retirement plan and/or
education plan contribution if the subject is successfully
mentored, and expected lifetime costs to society; obtaining
approval for the amount of financial incentive from the oversight
board; and transmitting a notice to the relevant governmental
agency about the approved amount.
2. The method of claim 1, wherein the financial incentive is
provided in the form of a payment to the mentor's retirement plan
and/or education plan, wherein amount of the cash payment is
calculated as a percentage of the at-risk subject's income tax
payment, retirement plan contribution, and/or education plan
contribution, and wherein said notice causes the relevant
governmental agency to issue a payment for the approved amount.
3. The method of claim 2, wherein the notice is subject to review
and approval by the relevant governmental agency and wherein the
relevant governmental agency issues a payment for the amount
approved by the relevant governmental agency.
4. The method of claim 3, wherein said notice causes the relevant
governmental agency to electronically deposit the approved amount
into a bank account designated by the mentor.
5. The method of claim 1, wherein the financial incentive is
provided in the form of (1) an income tax credit or (2) a
contribution to mentor's retirement plan and/or education plan, and
wherein said notice causes the relevant governmental agency to (1)
issue a notification to the mentor of an income tax credit for the
approved amount and electronically enter the income tax credit on
the mentor's tax record, or (2) issue a notification to the mentor
of a contribution to mentor's retirement plan and/or education plan
for the approved amount and electronically transfer funds to the
mentor's retirement plan and/or education plan.
6. The method of claim 5, wherein the notice is subject to review
and approval by the relevant governmental agency and/or oversight
board.
7. The method of claim 1, further comprising the step of:
retrieving, via the one or more computer processors, information
about how to reduce risks associated with one or more of the
plurality of risk factors from a database stored on a memory
device, and electronically delivering, via the one or more computer
processors, the information to the oversight board and/or a mentor
approved by the oversight board.
8. The method of claim 1, further comprising the step of:
retrieving, via the one or more computer processors, information
about how to reduce risks associated with one or more of the
plurality of risk factors from a database stored on a memory
device, and electronically delivering, via the one or more computer
processors, an alert to the mentor over a wireless communication
channel to a wireless device associated with the mentor, wherein
the alert activates an application on the wireless device that
causes the wireless device to connect, via Internet, to the one or
more computer processors and download said information.
9. The method of claim 1, wherein the plurality of risk factors
comprise one or more of the factors selected from the group
consisting of age, gender, race, weight, height, job history,
history of traffic violations, alcohol consumption, drug use
history, personal medical history, academic performance in school,
attendance history at school, appropriateness of behavior at
school, extra-curricular activities, gang involvement, personality
assessment, assessment of siblings and/or parents and/or guardians,
probability of dropping out of school, probability of becoming
pregnant, probability of committing a crime, probability of using
illegal drugs, job history, probability of becoming habitually
unemployed, and probability of returning to prison.
10. The method of claim 1, wherein the risk database includes
information on a plurality of at-risk people, risk factors
associated with each at-risk people, and follow-up information on
one or more of the plurality of at-risk people.
11. A system for providing mentoring service to at-risk people,
comprising: one or more computer processors; and one or more
tangible, non-transitory computer readable media accessible by the
one or more computer processors, wherein the one or more tangible,
non-transitory computer readable media comprise instructions that,
when executed by the one or more processors, causes the one or more
processors to perform: receiving, via a user interface of an
application executing on the one or more computer processors, a
risk profile concerning an at-risk subject, wherein the risk
profile comprises a plurality of risk factors and their expected
lifetime costs to society; assigning, via the one or more computer
processors, a risk point value to each of the plurality of risk
factors based on a severity level of the subject's risk factors and
their expected lifetime cost to society and a risk point matrix
stored on a memory device accessible by the one or more computer
processors, wherein the risk point matrix is determined by
evaluating a large scale data base reflecting risks of other
subjects compared to their later successes or failures and expected
lifetime costs to society; determining, via the one or more
computer processors, a total risk point value of the at-risk
subject via the one or more computer processors; accepting the
at-risk subject as a mentee candidate, if the total risk point
value satisfies a pre-determined threshold value; searching, via
the one or more computer processors, a mentor candidate database
comprising a plurality of mentor profiles, wherein each mentor
profile comprises personal data, mentoring experience and a
plurality of mentor matching tags; assigning, via the one or more
computer processors, at least one mentor candidate to the mentee
candidate; wherein the at least one mentor candidate is selected
using an artificial intelligence process implemented by an
artificial intelligence module; submitting the at least one
assigned mentor candidate to an oversight board for approval;
receiving, via the user interface of the application, the at-risk
subject's progress report after the establishment of a
mentor-mentee relationship; comparing, via the one or more computer
processors, the at-risk subject's progress to the progress achieved
by other at-risk individuals and their success odds or ex-convict
progress and success odds stored on a memory device accessible by
the one or more computer processors; obtaining, via the one or more
computer processors, (1) the at-risk subject's income tax records
from relevant governmental agencies, and/or (2) the at-risk
subject's retirement plan contribution information, and/or
education plan contribution information from relevant financial
institutions; determining, via the one or more computer processors,
a financial incentive to the mentor based on result of the
comparing step, the at-risk subject's income tax records, the
at-risk subject's retirement plan contribution information and/or
education plan contribution information, and expected lifetime
costs to society; obtaining approval for the amount of financial
incentive from the oversight board; and transmitting a notice to
the relevant governmental agency about the approved amount.
12. The system of claim 11, wherein the financial incentive is
provided in the form of a cash payment to the mentor, wherein
amount of the cash payment is calculated as a percentage of (1) the
at-risk subject's income tax payment, and/or (2) the at-risk
subject's contributions to the at-risk subject's retirement plan
and/or education plan, and/or (3) the at-risk subject's avoidance
of negative outcomes and events that results in costs to
society.
13. The system of claim 11, wherein the notice is subject to review
and approval by the relevant governmental agency and wherein the
relevant governmental agency issues a payment for the amount
approved by the relevant governmental agency.
14. The system of claim 13, wherein said notice causes the relevant
governmental agency to electronically deposit the approved amount
into a bank account designated by the mentor.
15. The system of claim 11, wherein the financial incentive is
provided in the form of a contribution to mentor's retirement plan
and/or education plan, and wherein said notice causes the relevant
governmental agency to issue a notification to the mentor of a
contribution to mentor's retirement plan and/or education plan for
the approved amount and electronically transfer the contribution to
mentor's retirement plan and/or education plan.
16. The system of claim 11, wherein the one or more tangible,
non-transitory computer readable media comprise instructions that,
when executed by the one or more processors, causes the one or more
processors to perform the step of: retrieving, via the one or more
computer processors, information about how to reduce risks
associated with one or more of the plurality of risk factors from a
database stored on a memory device, and electronically delivering,
via the one or more computer processors, the information to the
oversight board and/or a mentor approved by the oversight board
and/or the mentee's parents or guardians.
17. The system of claim 16, wherein the information is delivered
over a wireless communication channel to a wireless device
associated with the mentor.
18. The system of claim 17, wherein the one or more tangible,
non-transitory computer readable media comprise instructions that,
when executed by the one or more processors, causes the one or more
processors to perform the step of: retrieving, via the one or more
computer processors, information about how to reduce risks
associated with one or more of the plurality of risk factors from a
database stored on a memory device, and electronically delivering,
via the one or more computer processors, an alert to the mentor
over a wireless communication channel to a wireless device
associated with the mentor, wherein the alert activates an
application on the wireless device that causes the wireless device
to connect, via Internet, to the one or more computer processors
and download said information.
19. A tangible, non-transitory computer readable medium, comprising
instructions that, when executed by a computer processor, causes
the processor to perform: receiving, via a user interface on a
computer, a risk profile concerning an at-risk subject, wherein the
risk profile comprises a plurality of risk factors; assigning, via
a computer processor, a risk point value to each of the plurality
of risk factors based on severity level of the subject's risk
factors and a risk point matrix stored on a memory device
accessible by the computer processor, wherein the risk point matrix
is determined by evaluating a risk database reflecting risks of
other subjects compared to their later successes or failures and
estimated lifetime cost to society; determining, via the computer
processor, a total risk point value of the at-risk subject via the
one or more computer processors; accepting the at-risk subject as a
mentee candidate, if the total risk point value satisfies a
pre-determined threshold value; searching, via the computer
processor, a mentor candidate database comprising a plurality of
mentor profiles, wherein each mentor profile comprises personal
data, mentoring experience and a plurality of mentor matching tags;
assigning, via the computer processor, at least one mentor
candidate to the mentee candidate, wherein the at least one mentor
candidate is selected based on a match between the mentee matching
tags and the mentor matching tags, where in the match is performed
using a matching algorithm; submitting the at least one assigned
mentor candidate to an oversight board for approval; receiving, via
the user interface of the application, the at-risk subject's
progress report after the establishment of a mentor-mentee
relationship; comparing, via the computer processor, the at-risk
subject's progress to at-risk individual success odds or ex-convict
success odds stored on a memory device accessible by the computer
processor; obtaining, via the computer processor, (1) the at-risk
subject's income tax records from relevant governmental agencies,
and/or (2) the at-risk subject's retirement plan contribution
information, and/or education plan contribution information from
relevant financial institutions; determining, via the one or more
computer processors, a financial incentive to the mentor based on
result of the comparing step, the at-risk subject's income tax
records, the at-risk subject's retirement plan contribution
information and/or education plan contribution information, and
expected lifetime costs to society; determining, via the computer
processor, a financial incentive to the mentor based on result of
the comparing step and the at-risk subject's income tax records and
estimated lifetime costs to society; obtaining approval for the
amount of financial incentive from the oversight board; and
transmitting a notice to the relevant governmental agencies about
the approved amount, wherein the financial incentive is provided in
the form of a cash payment to the mentor, wherein amount of the
cash payment is calculated as a percentage of the at-risk subject's
income tax payment, the at-risk subject's retirement plan
contribution information and/or education plan contribution each
year or reflects payment based on the mentee's achievement of one
or more milestones or avoidance of events that results in costs to
society and wherein said notice causes the relevant governmental
agency to issue a payment for the approved amount, and wherein the
tangible, non-transitory, computer readable medium, further
comprises instructions that, when executed by a computer processor,
causes the processor to perform the step of: improving the risk
database functionality using ontology related artificial
intelligence.
20. The tangible, non-transitory computer readable medium of claim
19, wherein the financial incentive is provided in the form of an
income tax credit for the mentor or a contribution to the mentor's
retirement plan and/or education plan, and wherein said notice
causes the relevant governmental agency to issue a notification to
the mentor of the income tax credit, or the contribution to the
mentor's retirement plan and/or education plan, for the approved
amount and electronically enter the income tax credit on the
mentor's tax record or electronically transfer the contribution to
the mentor's retirement plan and/or education plan.
21. The tangible, non-transitory computer readable medium of claim
19, further comprising instructions that, when executed by the
computer processor, causes the computer processor to perform the
step of: retrieving, via the one or more computer processors,
information about how to reduce risks associated with one or more
of the plurality of risk factors from a database stored on a memory
device, and electronically delivering, via the computer processor,
an alert to the mentor over a wireless communication channel to a
wireless device associated with the mentor, wherein the alert
activates an application on the wireless device that causes the
wireless device to connect, via Internet, to the computer processor
and download said information.
22. The tangible, non-transitory computer readable medium of claim
19, wherein the financial incentive is provided in the form of
points provided to the mentor, wherein amount of the points payment
is calculated as a percentage of (1) the at-risk subject's income
tax payment, and/or (2) the at-risk subject's contributions to the
at-risk subject's retirement plan and/or education plan, and/or (3)
the at-risk subject's avoidance of negative outcomes and events
that results in costs to society.
Description
[0001] This application is a continuation application of U.S.
application Ser. No. 15/691,253, filed on Aug. 30, 2017, which is a
continuation-in-part application of U.S. application Ser. No.
15/623,206, filed on Jun. 14, 2017, now U.S. Pat. No. 10,133,998,
which is a continuation-in-part application of U.S. application
Ser. No. 15/373,869, filed on Dec. 9, 2016, now U.S. Pat. No.
10,068,194, which is a continuation application of U.S. application
Ser. No. 15/266,384, filed on Sep. 15, 2016. U.S. application Ser.
No. 15/623,206, filed on Jun. 14, 2017 is also a
continuation-in-part application of U.S. application Ser. No.
15/337,799, filed on Oct. 28, 2016, now U.S. Pat. No. 10,019,688,
which is a continuation application of U.S. application Ser. No.
15/266,384, filed on Sep. 15, 2016. The entirety of the
aforementioned applications is incorporated herein by
reference.
FIELD
[0002] This disclosure is generally related to a system and method
for processing information and mentoring people. In particular,
this disclosure is related to a system and method for utilizing
mentor and mentee information to improve matching between mentors
and mentees, to improve the success of relationships between
mentors and mentees, and to provide incentives to mentors.
BACKGROUND
[0003] Even though current mentoring systems operate on a very
small scale for a short period of time, the mentoring systems
provide a substantial impact on, for example, children from
troubled backgrounds and people who have recently been released
from prison. However, in the current mentoring systems, no
quantitative method exists for pairing a potential mentee with a
potential mentor with a view to maximizing a probability of a
successful mentor-mentee relationship on a long-term basis.
Additionally, in current mentorship systems, the mentor is not
financially compensated for being a successful mentor and has
little ongoing incentive to establish a long term supportive
relationship with the mentee. Moreover, there are many more people
who need mentors than can be satisfied by the current mentor
volunteers.
[0004] Accordingly, there is a need for systems and methods for
improving the pairing between mentors and mentees. There is also a
need for systems and methods for financially compensating
successful mentors on a long-term basis in order to greatly
increase the supply of mentors.
SUMMARY
[0005] One aspect of the present application relates to a method
for processing information for providing mentoring service to
at-risk people, comprising the steps of: receiving, via a user
interface of an application executing on one or more computer
processors, a personal profile concerning an at-risk subject,
wherein the personal profile comprises personal data, a risk
profile comprises a plurality of risk factors, and a plurality of
mentee matching tags; assigning, via the one or more computer
processors, a risk point value to each of the plurality of risk
factors based on severity level of the subject's risk factors and a
risk point matrix stored on a memory device accessible by the one
or more computer processors, wherein the risk point matrix is
determined by evaluating a large scale database reflecting risks of
other subjects compared to their later successes or failures, and
estimated lifetime costs to society; determining, via the one or
more computer processors, a total risk point value of the at-risk
subject via the one or more computer processors; accepting the
at-risk subject as a mentee candidate, if the total risk point
value satisfies a pre-determined threshold value; searching, via
the one or more computer processors, a mentor candidate database
comprising a plurality of mentor profiles, wherein each mentor
profile comprises personal data, mentoring experience and a
plurality of mentor matching tags; assigning, via the one or more
computer processors, at least one mentor candidate to the mentee
candidate, wherein the at least one mentor candidate is selected
based on a match between the mentee matching tags and the mentor
matching tags, where in the match is performed using a matching
algorithm; submitting the at least one assigned mentor candidate to
an oversight board for approval; receiving, via the user interface
of the application, the at-risk subject's progress report after the
establishment of a mentor-mentee relationship; comparing, via the
one or more computer processors, the at-risk subject's progress to
at-risk individual's success odds or ex-convict's success odds
stored on a memory device accessible by the one or more computer
processors; obtaining, via the one or more computer processors, (1)
the at-risk subject's income tax records from relevant governmental
agencies, (2) the at-risk subject's retirement plan (e.g., 401(k))
contribution information, and/or education plan (e.g., the 529
plan) contribution information from relevant financial
institutions; determining, via the one or more computer processors,
a financial incentive to the mentor based on result of the
comparing step, the at-risk subject's income tax records, the
at-risk subject's retirement plan contribution information and/or
education plan contribution information, and expected lifetime
costs to society; obtaining approval for the amount of financial
incentive from the oversight board; and transmitting a notice to
the relevant governmental agency about the approved amount.
[0006] Another aspect of the present application relates to a
system for processing information for providing mentoring service
to at-risk people, comprising: one or more computer processors; and
one or more tangible, non-transitory computer readable media
accessible by the one or more computer processors, wherein the one
or more tangible, non-transitory computer readable media comprise
instructions that, when executed by the one or more processors,
cause the one or more processors to perform: receiving, via a user
interface of an application executing on the one or more computer
processors, a risk profile concerning an at-risk subject, wherein
the risk profile comprises a plurality of risk factors and their
expected lifetime costs to society; assigning, via the one or more
computer processors, a risk point value to each of the plurality of
risk factors based on severity level of the subject's risk factors
and their expected lifetime cost to society and a risk point matrix
stored on a memory device accessible by the one or more computer
processors, wherein the risk point matrix is determined by
evaluating a large scale data base reflecting risks of other
subjects compared to their later successes or failures and expected
lifetime costs to society; determining, via the one or more
computer processors, a total risk point value of the at-risk
subject via the one or more computer processors; accepting the
at-risk subject as a mentee candidate, if the total risk point
value satisfies a pre-determined threshold value; searching, via
the one or more computer processors, a mentor candidate database
comprising a plurality of mentor profiles, wherein each mentor
profile comprises personal data, mentoring experience and a
plurality of mentor matching tags; assigning, via the one or more
computer processors, at least one mentor candidate to the mentee
candidate, wherein the at least one mentor candidate is selected
based on a match between the mentee matching tags and the mentor
matching tags, where in the match is performed using a matching
algorithm; submitting the at least one assigned mentor candidate to
an oversight board for approval; receiving, via the user interface
of the application, the at-risk subject's progress report after the
establishment of a mentor-mentee relationship; comparing, via the
one or more computer processors, the at-risk subject's progress to
the progress achieved by other at-risk individuals and their
success odds or ex-convict progress and success odds stored on a
memory device accessible by the one or more computer processors;
obtaining, via the one or more computer processors, (1) the at-risk
subject's income tax records from relevant governmental agencies,
and/or (2) the at-risk subject's retirement plan contribution
information, and/or education plan contribution information from
relevant financial institutions; determining, via the one or more
computer processors, a financial incentive to the mentor based on
result of the comparing step, the at-risk subject's income tax
records, the at-risk subject's retirement plan contribution
information and/or education plan contribution information, and
expected lifetime costs to society; obtaining approval for the
amount of financial incentive from the oversight board; and
transmitting a notice to the relevant governmental agency about the
approved amount.
[0007] Another aspect of the present application relates to a
tangible, non-transitory computer readable medium, comprising
instructions that, when executed by a computer processor, cause the
processor to perform: receiving, via a user interface on a
computer, a risk profile concerning an at-risk subject, wherein the
risk profile comprises a plurality of risk factors; assigning, via
a computer processor, a risk point value to each of the plurality
of risk factors based on severity level of the subject's risk
factors and a risk point matrix stored on a memory device
accessible by the computer processor, wherein the risk point matrix
is determined by evaluating a large scale data base reflecting
risks of other subjects compared to their later successes or
failures and estimated lifetime cost to society; determining, via
the computer processor, a total risk point value of the at-risk
subject via the one or more computer processors; accepting the
at-risk subject as a mentee candidate, if the total risk point
value satisfies a pre-determined threshold value; searching, via
the computer processor, a mentor candidate database comprising a
plurality of mentor profiles, wherein each mentor profile comprises
personal data, mentoring experience and a plurality of mentor
matching tags; assigning, via the computer processor, at least one
mentor candidate to the mentee candidate, wherein the at least one
mentor candidate is selected based on a match between the mentee
matching tags and the mentor matching tags, where in the match is
performed using a matching algorithm; submitting the at least one
assigned mentor candidate to an oversight board for approval;
receiving, via the user interface of the application, the at-risk
subject's progress report after the establishment of a
mentor-mentee relationship; comparing, via the computer processor,
the at-risk subject's progress to at-risk individual success odds
or ex-convict success odds stored on a memory device accessible by
the computer processor; obtaining, via the computer processor, (1)
the at-risk subject's income tax records from relevant governmental
agencies, and/or (2) the at-risk subject's retirement plan
contribution information, and/or education plan contribution
information from relevant financial institutions, and/or (3) the
at-risk subject's criminal and prison records and their related
costs, and/or (4) the amounts received by the at-risk subject from
welfare and/or food stamp payments; determining, via the one or
more computer processors, a financial incentive to the mentor based
on result of the comparing step, the at-risk subject's income tax
records, the at-risk subject's retirement plan contribution
information and/or education plan contribution information, and
expected lifetime costs to society from criminal activity, prison
costs, court costs, welfare payments, food stamp payments, and
similar costs to society; determining, via the computer processor,
a financial incentive to the mentor based on result of the
comparing step and the at-risk subject's income tax records and
estimated lifetime costs to society; obtaining approval for the
amount of financial incentive from the oversight board; and
transmitting a notice to the relevant governmental agencies about
the approved amount, wherein the financial incentive is provided in
the form of a cash payment to the mentor, wherein amount of the
cash payment is calculated as a percentage of the at-risk subject's
income tax payment, the at-risk subject's retirement plan
contribution information and/or education plan contribution each
year or reflects payment based on the mentee's achievement of one
or more milestones or avoidance of events that result in costs to
society and wherein said notice causes the relevant governmental
agency to issue a payment for the approved amount.
BRIEF DESCRIPTION OF THE DRAWINGS
[0008] The present application can be better understood by
reference to the following drawings, wherein like references
numerals represent like elements. The drawings are merely exemplary
to illustrate certain features that may be used singularly or in
any combination with other features and the present invention
should not be limited to the embodiments shown.
[0009] FIG. 1 shows an embodiment of the system of present
application
[0010] FIG. 2 shows an embodiment of the database structure of the
present application.
[0011] FIG. 3 is a flow chart showing exemplary steps of the method
of the present application.
DETAILED DESCRIPTION
[0012] The following detailed description is presented to enable
any person skilled in the art to make and use the object of this
application. For purposes of explanation, specific nomenclature is
set forth to provide a thorough understanding of the present
application. However, it will be apparent to one skilled in the art
that these specific details are not required to practice the
subject of this application. Descriptions of specific applications
are provided only as representative examples. The present
application is not intended to be limited to the embodiments shown,
but is to be accorded the widest possible scope consistent with the
principles and features disclosed herein.
[0013] This description is intended to be read in connection with
the accompanying drawings, which are to be considered part of the
entire written description of this application. The drawing figures
are not necessarily to scale and certain features of the
application may be shown exaggerated in scale or in somewhat
schematic form in the interest of clarity and conciseness.
[0014] As used herein, the term "at-risk people" refers to
individuals, groups, populations or sub-populations who are
considered to have a higher probability of failing socially,
economically, academically or morally. The term may be applied to
those who face circumstances that could jeopardize their ability to
complete school, get or retain employment, or avoid criminal
activity. Such people may have higher than average rates of
homelessness, poverty, incarceration, teenage pregnancy, serious
health issues, domestic violence, transiency, gang activity, drug
use, or other criminal activity or conditions. Such people may also
have learning disabilities, low test scores, disciplinary problems,
grade retentions, or other learning-related factors that have
adverse effects.
[0015] As used herein, "recidivism" refers to repeated or habitual
relapse into a behavior such as crime, or the chronic tendency
toward repetition of criminal or antisocial behavior patterns.
[0016] As used herein, the term "mentee" refers to an at-risk
individual who agrees to accept the instruction, guidance, support
and encouragement of an individual tasked with aiding the mentee
become a successful member of society.
[0017] As used herein, the term "mentor" refers to an individual
who provides instruction, guidance, support and encouragement to a
mentee for the purpose of aiding the mentee to become a successful
member of society.
[0018] As used herein, the term "wireless" means any wireless
signal, data, communication, or other interface including without
limitation Wi-Fi, Bluetooth, 3G, HSDPA/HSUPA, TDMA, CDMA (e.g.,
IS-95A, WCDMA, etc.), FHSS, DSSS, GSM, PAN/802.15, Wi-MAX (802.16),
802.20, narrowband/FDMA, OFDM, PCS/DCS, analog cellular, CDPD,
satellite systems, millimeter wave or microwave systems, acoustic,
and infrared (i.e., IrDA).
[0019] As used herein, the terms "Internet" and "internet" are used
interchangeably to refer to inter-networks including, without
limitation, the Internet.
[0020] As used herein, the term "memory" includes any type of
integrated circuit or other storage device adapted for storing
digital data including, without limitation, ROM, PROM, EEPROM,
DRAM, SDRAM, DDR/2 SDRAM, EDO/FPMS, RLDRAM, SRAM, "flash" memory
(e.g., NAND/NOR), and PSRAM.
[0021] As used herein, the term "computer processor" refers
generally to all types of digital processing devices including,
without limitation, digital signal processors (DSPs), reduced
instruction set computers (RISC), general-purpose (CISC)
processors, microprocessors, gate arrays (e.g., FPGAs), PLDs,
reconfigurable compute fabrics (RCFs), array processors, and
application-specific integrated circuits (ASICs). Such digital
processors may be contained on a single unitary IC die, or
distributed across multiple components.
[0022] As used herein, the term "at-risk subject's income tax
records" include the at-risk subject's federal, state and/or city
income tax records, and, in some embodiments, also include the
at-risk subject's family's federal, state and/or city income tax
records.
[0023] As unused herein, the term "costs to society" include costs
of welfare, food stamps, unemployment payments, Medicaid, teenage
pregnancy, drug use, crime, prison, lack of income tax payments,
failing to graduate from high school, etc. The term "lifetime costs
to society" include lifetime costs of welfare, food stamps,
unemployment payments, Medicaid, teenage pregnancy, drug use,
crime, prison, lack of income tax payments, failing to graduate
from high school, etc.
[0024] One basic premise that lies behind the present disclosure is
that "success breeds success." Individuals who have exposure to,
and guidance from, persons who are successful have a greater chance
of becoming successful themselves. Individuals who lack successful
role models in their lives experience a greater likelihood of
failure or recidivism. Accordingly, the present application seeks
to link at-risk children and adults (referred to together as
"at-risk individuals") with mentors who will have a lifelong
economic motivation to ensure their success. The mentors would be
compensated based on the future success of their mentees, such as
with some fraction of the income tax payments by their mentees or
some other metric reflective of their success as productive
citizens. In this manner, the mentors will have a strong ongoing
multi-year motivation to advise, coach, implore, train, and
otherwise influence the success of their mentees. They would want
them to get educated, avoid crime and drug use, and would even be
motivated to help them get good jobs. They would be interested in
maximizing their long-term success. They may share their wisdom, or
offer them jobs, or recommend them for certain positions or
opportunities. Their friends and relatives may also be motivated to
help the mentee since it would be beneficial to the mentor. The
same principles can be applied to prisoners who have served their
time. For such mentors, the economic incentive could be based on
income tax payments by their mentees over some extended number of
years or it could also include a bonus for reaching certain
milestones (such as high school graduation, college attendance,
college graduation, avoidance of future crime, avoidance of future
jail time, etc.) or for each day or week or month or year of crime
free activity by their mentees and their avoidance of costs to
society.
[0025] Based on the success of various social and charity programs
that lack any type of long term economic incentive, applicant has
come to the conclusion that a properly constructed large scale
effort could radically improve the success rate of at-risk people
of all ages, while improving race relations and reducing crime,
welfare expenses (and other costs to society), and reducing the
national debt by trillions of dollars.
[0026] Method for Processing Information for Providing Mentoring
Service to At-Risk People
[0027] One aspect of the present application relates to a method
for processing information for providing mentoring service to
at-risk people. The method comprises the steps of: receiving, via a
user interface of an application executing on one or more computer
processors, a risk profile concerning an at-risk subject, wherein
the risk profile comprises a plurality of risk factors; assigning,
via one or more computer processors, a risk point value to each of
the plurality of risk factors based on severity level of the
subject's risk factors and a risk point matrix stored on a memory
device accessible by the one or more computer processors;
determining, via the application, a total risk point value of the
subject via the one or more computer processors; and when the total
risk point value reaches a predetermined threshold value, accepting
the at-risk subject as a mentee candidate. In some embodiments, a
higher severity level of a risk is assigned a higher risk point.
The total risk point value reaches the predetermined threshold
value if the total risk point value equals to or exceeds the
threshold value. In other embodiments, a higher severity level of a
risk is assigned a lower risk point. The total risk point value
reaches the predetermined threshold value if the total risk point
value equals to or below the threshold value.
[0028] Examples of the risk factors include, but are not limited
to, age, gender, race, weight, height, job history, history of
traffic violations, alcohol consumption, drug use history, personal
medical history, academic performance in school, attendance history
at school, appropriateness of behavior at school (such as avoiding
fighting, bullying, cheating or other disruptive behavior),
extra-curricular activities, gang involvement, personality
assessment, assessment of siblings and/or parents and/or guardians,
probability of dropping out of school, probability of becoming
pregnant, probability of committing a crime, probability of using
illegal drugs, job history, probability of becoming habitually
unemployed, probability of returning to prison, and other risk
factors.
[0029] In some embodiments, the risk point matrix is determined by
evaluating a risk database reflecting risks of other subjects
compared to their later successes or failures and estimated
lifetime cost to society. In some embodiments, the risk database
includes information on a large number of at-risk people, risk
factors associated with each at-risk people, and follow-up
information on each at-risk people. Such information is assembled
from various data sources, such as school records, DMV records,
police records and correction facility records, personal medical
records, financial transaction records and tax records. In some
embodiments, the method further comprises the step of generating a
database reflecting risks of other subjects compared to their later
successes or failures and estimated lifetime costs to society.
[0030] In some embodiments, ontology related artificial
intelligence is utilized to improve the risk database
functionality. Ontology is a technical art in computer technologies
that compartmentalizes variables required in complex computational
processes and establishes the fundamental relationships among the
variables. Since the original data are collected from various
sources in difference settings (e.g., school records vs. police
records), a semantic variable may have different names in different
data sources, making this variable difficult to identify. In some
embodiments, the risk point matrix is derived from the risk
database using an artificial intelligence method that comprises the
steps of identifying semantic information from an individual
information source, analyzing and comparing the semantic
information to semantic information identified from one or more
other information sources, finding possible relationships between
semantic information from different information sources and
establishing an ontology for the risk database. In some
embodiments, the artificial intelligence method is implemented by
an artificial intelligence module.
[0031] As used herein, the term "user interface of an application"
refers to, without limitation, any visual, graphical, tactile,
audible, sensory, digital or other means of providing information
to and/or receiving information from a user or other entity. A user
interface of an application includes means for receiving
information from a tangible storage media, such as a flash drive,
or from the internet.
[0032] In some embodiments, the method further comprises the steps
of: assigning, via the one or more computer processors, a mentor
candidate to the at-risk subject, wherein the mentor is selected
from a mentor candidate database on a memory device accessible by
the one or more computer processors; and approving the assigned
mentor by an oversight board.
[0033] In some embodiments, the oversight board may include at
least one member from local community, at least one member with
juvenile correction experience and/or at least one member from the
criminal justice system. In some embodiments, the oversight board
also includes a representative from a local, state or federal
government. In some embodiments, the oversight board includes a
representative from a tax agency of the local, state or federal
government.
[0034] In some embodiments, the method further comprises the steps
of searching, via the one or more computer processors, a mentor
candidate database comprising a plurality of mentor profiles,
wherein each mentor profile comprises personal data, mentoring
experience and a plurality of mentor matching tags; and assigning,
via the one or more computer processors, at least one mentor
candidate to the mentee candidate, wherein the at least one mentor
candidate is selected based on a match between the mentee matching
tags and the mentor matching tags, where in the match is performed
using a matching algorithm.
[0035] In some embodiments, the mentor candidate database includes
mentors' personal information, such as gender, age, race, marital
status, health status, education, work history and mentoring
experience and performance. In some embodiments, the initial
assignment of the mentor is determined by a computer program that
matches the at-risk candidate with mentors in the mentor candidate
database using a set of mentor matching criteria.
[0036] In some embodiments, the initial assignment of the mentor is
determined by an artificial intelligence module based on the risk
profile of the at-risk subject and information stored in the mentor
qualification database. In some embodiments, the artificial
intelligence module comprises a user interface that allows a
programmer to build an artificial intelligence model, train the
artificial intelligence model to provide a trained artificial
intelligence model, and deploy the trained artificial intelligence
model for mentor selection.
[0037] In some embodiments, the artificial intelligence module is
configured to train the artificial intelligence model in one or
more training cycles with training data from one or more training
data sources, such as data from the mentor candidate database. In
some embodiments, the artificial intelligence module proposes an
artificial intelligence model from an assembly code that is
generated from a source code written in a pedagogical programming
language. The source code includes a mental model of one or more
concept modules to be learned by the artificial intelligence model
using the training data and curricula of one or more lessons for
training the artificial intelligence model on the one or more
concept modules. Each of the one or more lessons is structured to
optionally use a different flow of the training data. The training
lesson may include selecting mentors and/or mentees based on
certain factors including, in the case of mentors, their relative
career and lifetime success, their emotional maturity, their job
and family status, their ability and willingness to dedicate the
time necessary to be a successful mentor, among other factors, and
in the case of mentees, their age, family situation, race, school
performance, school attendance, school dropout rates, gang
involvement or temptations, drug use, criminal activity, and
maturity, among other factors.
[0038] The artificial intelligence module allows for a learning
process that improves the odds of a successful mentor-mentee
matching.
[0039] In some embodiments, the method further comprises the steps
of receiving, via the user interface of the application, the
at-risk subject's progress report after the establishment of a
mentor-mentee relationship; and comparing, via the one or more
computer processors, the at-risk subject's progress to the progress
achieved by other at-risk individuals and their success odds or
ex-convict progress and success odds stored on a memory device
accessible by the one or more computer processors.
[0040] In some embodiments, the method further comprises the steps
of: retrieving, via the one or more computer processors,
information about how to reduce risks associated with one or more
of the plurality of risk factors from a risk reduction database
stored on a memory device, and electronically delivering, via the
one or more computer processors, the information to the oversight
board and/or a mentor approved by the oversight board and/or
parents or guardians of the mentee. In some embodiments, the
information is delivered over a wireless communication channel to a
wireless device associated with the mentor.
[0041] In some embodiments, the risk reduction database is updated
periodically with new methods and/or devices for reducing risks. In
some embodiments, the risk reduction database is supplemented with
a searching program that searches Internet continuously for the
appearance of new risks, as well as improved or new methods/devices
for reducing/avoiding risks. In some embodiments, ontology related
artificial intelligence is utilized to improve the risk reduction
database functionality. In some embodiments, the risk reduction
database is part of the risk database.
[0042] In some other embodiments, the method further comprises the
steps of: retrieving, via the one or more computer processors,
information about how to reduce risks associated with one or more
of the plurality of risk factors from a database stored on a memory
device, and electronically delivering, via the one or more computer
processors, an alert to the mentor (or other appropriate interested
parties) over a wireless communication channel to a wireless device
associated with the mentor, wherein the alert activates an
application on the wireless device that causes the wireless device
to connect, via Internet, to the one or more computer processors
and download said information.
[0043] In some embodiments, the method further comprises the steps
of: receiving, via a user interface of the application, the at-risk
subject's progress report after the establishment of a
mentor-mentee relationship; comparing, via one or more computer
processors, the at-risk subject's progress to at-risk individual
success odds or ex-convict success odds stored on a memory device
accessible by the one or more computer processors; providing, via
the one or more computer processors, a financial incentive to the
mentor based on result of the comparing step; and maintaining, via
the one or more computer processors, a mentor incentive database,
wherein the mentor incentive database is stored on a tangible
medium accessible by the one or more computer processors. In some
embodiments, the financial incentive calculation is subject to
review by an oversight board so that, if necessary, adjustments can
be made for mentees who do not attract appropriate mentors on a
timely basis. The oversight board review ensures that all relevant
factors are considered to balance supply and demand at a particular
point in time within a specific geography for mentors and
mentees.
[0044] In some embodiments, the progress report includes the
mentee's current educational status, marital status, various risk
factor status, health status, mental status, etc. In some
embodiments, the computer processor assigns a point value to each
status so as to obtain a total point for each progress report. In
some embodiments, the method further includes the steps of
identifying semantic information from the at-risk subject's
progress report. In some embodiments, the progress report is every
1, 2, 3, 4 or 6 months or other appropriate time interval. The
at-risk subject's progress report may be submitted by the at-risk
subject and/or the at-risk subject's parent or guardian or
teacher.
[0045] In some embodiments, the semantic information from the
at-risk subject's progress report is used to determine the at-risk
subject's at-risk individual success odds or ex-convict success
odds are calculated using the artificial intelligence module based
on information stored in an at-risk individual success odds
database or ex-convict success odds database.
[0046] In some embodiments, the financial incentive is provided in
the form of (1) a cash payment to the mentor, (2) a contribution to
the mentor's retirement plan (e.g., the 401(k) account), (3) a
contribution to the mentor's educational savings plan (e.g., the
529 plan and educational savings account), (4) an income tax credit
in the form of a federal income tax credit, state income tax
credit, city income tax credit, or combinations thereof, and/or
"Points" that could be provided by external corporations which want
to promote the success of the mentoring program. As used herein,
the term "Points" may be generally described as a representation of
value that could be converted into goods or services, such as with
airline "Points." As used herein, the term "retirement plan" may be
generally described as including "Defined Contribution Benefit
Plans" with the employee or his/her assignees, etc., as
beneficiaries of the plan. These plans include, but are not limited
to, 401(k) plans, 403(b) plans, employee stock ownership plans,
Simple Individual Retirement Accounts ("Simple IRAs"), simplified
employee pension plans (SEPs) and profit sharing plans. The amount
of the financial incentive is calculated based on the at-risk
subject's behavior and/or the at-risk subject's accomplishments
and/or the at-risk subject's income tax payment or estimated
lifetime costs to society. In some embodiments, the amount of the
financial incentive is calculated based on the at-risk subject's
behavior and/or the at-risk subject's personal income tax payment
and/or family income tax payment in the past year. The subject's
income tax payment may include the federal income tax payment,
state income tax payment, city income tax payment or combinations
thereof. In some embodiments, the amount of the financial incentive
is calculated based on the at-risk subject's projected income tax
payment and/or the at-risk subject's estimated lifetime cost to
society. In some embodiments, the amount of the financial incentive
is calculated based on the at-risk subject's contribution to a
retirement plan, such as the 401(k) plan. In some embodiments, the
amount of the financial incentive is calculated based on the
at-risk subject's contribution to an educational plan, such as the
529 plan. In some embodiments, the amount of the financial
incentive is calculated based on one or more factors selected from
the at-risk subject's personal income tax payment, the at-risk
subject's family income tax payment, the at-risk subject's
contribution to a retirement plan (such as the 401(k)) and the
at-risk subject's contribution to an educational plan, such as the
529 plan.
[0047] In some embodiments, the financial incentive is provided to
the mentor as a bonus if the at-risk subject achieves certain
goals.
[0048] In some embodiments, the method further comprises one or
more of the following the steps: obtaining the at-risk subject's
projected income tax payment and/or estimated lifetime costs to
society, preferably from relevant governmental agencies.
[0049] In some embodiments, the method further comprises the steps
of obtaining federal and/or local tax (e.g., state and city taxes)
payment information from the at-risk subject, and verifying the
federal and/or local tax (e.g., state and city taxes) payment
information with corresponding governmental agencies. In some
embodiments, the method further comprises the step of obtaining
permission from the at-risk subject to access the at-risk subject's
personal income or family income tax payment information from
relevant government agencies.
[0050] In some embodiments, the method further comprises the steps
of obtaining retirement plan contribution information and/or
educational savings plan information from the at-risk subject, and
verifying the retirement plan contribution and/or educational plan
contribution information with the relevant financial institutions.
In some embodiments, the method further comprises the step of
obtaining permission from the at-risk subject to access the at-risk
subject's retirement plan and/or educational plan information in
relevant financial institutions.
[0051] In some embodiments, the method further comprises the steps
of determining an amount of the cash payment and/or the bonus (in
the form of income tax credit) to the mentor, obtaining approval
from the oversight board, obtaining approval from relevant
governmental agency (e.g., IRS, state or city department of
taxation) and processing the amount approved by the governmental
agency for payment.
[0052] In some embodiments, the financial incentive is provided in
the form of points provided to the mentor, wherein the method
further comprises the step of calculating the amount of the points
payment as a percentage of (1) the at-risk subject's income tax
payment, and/or (2) the at-risk subject's contributions to the
at-risk subject's retirement plan and/or education plan, and/or (3)
the at-risk subject's avoidance of negative outcomes and events
that results in costs to society.
[0053] In some embodiments, the method further comprises the step
of transmitting a notification of payment to a department, company,
agency or financial institution that handles payments to the
mentors, wherein the notification causes the department, company,
agency or financial institution to process payment to the mentor
either reflective of the mentee's progress or as reimbursement for
spending by the mentor in support of the mentee.
[0054] In some embodiments, the method further comprises the step
of transmitting an alert of the cash payment to a mentor to a
relevant governmental agency (e.g., IRS) to cause the governmental
agency to issue a reimbursement for the cash payment. In some
embodiments, the method further comprises the step of transmitting
an alert of the issuance of an income tax credit to a mentor to a
relevant governmental agency (e.g., IRS) to cause the governmental
agency to enter the tax credit into the mentor's tax record.
[0055] In some embodiments, the method comprises the steps of
obtaining income tax payment information from relevant government
agencies or retirement/educational plan contribution information
from relevant companies, agencies or institutions, determining an
amount of the cash payment and/or the bonus (in the form of income
tax credit) to the mentor, obtaining approval from the oversight
board, notifying relevant governmental agencies (e.g., IRS, state
or city department of taxation) about the approved amount, wherein
the notification causes the relevant governmental agency to process
payment to the mentor and electronically deliver the payment to a
bank account designated by the mentor.
[0056] System for Processing Information for Providing Mentoring
Service to At-Risk People
[0057] Another aspect of the present application relates to a
system that collects and/or processes information, and uses the
information to provide mentoring service to at-risk people. The
system comprises one or more computer processors; and one or more
tangible, non-transitory computer readable media accessible by the
one or more computer processors, wherein the one or more tangible,
non-transitory computer readable media comprise instructions that,
when executed by the one or more processors, cause the one or more
processors to perform the following functions: receiving, via a
user interface of an application executing on the one or more
computer processors, a risk profile concerning an at-risk subject,
wherein the risk profile comprises a plurality of risk factors;
assigning a risk point value to each of the plurality of risk
factors based on severity level of the subject's risk factors and a
risk point matrix stored on one or more tangible, non-transitory
computer readable media accessible by the one or more computer
processors; determining a total risk point value of the subject;
and when the total risk point value reaches a predetermined
threshold value, accepting the at-risk subject as a mentee
candidate. In some embodiments, the risk point matrix is determined
by evaluating a large scale data base reflecting risks of other
subjects compared to their later successes or failures and
estimated lifetime costs to society.
[0058] In some embodiments, the one or more tangible,
non-transitory computer readable media comprise instructions that,
when executed by the one or more processors, cause the one or more
processors to perform one or more of the following steps:
searching, via the one or more computer processors, a mentor
candidate database comprising a plurality of mentor profiles,
wherein each mentor profile comprises personal data, mentoring
experience and a plurality of mentor matching tags; assigning, via
the one or more computer processors, at least one mentor candidate
to the mentee candidate, wherein the at least one mentor candidate
is selected based on a match between the mentee matching tags and
the mentor matching tags, wherein the match is performed using a
matching algorithm; submitting the at least one assigned mentor
candidate to an oversight board for approval; receiving, via the
user interface of the application, the at-risk subject's progress
report after the establishment of a mentor-mentee relationship;
comparing, via the one or more computer processors, the at-risk
subject's progress to the progress achieved by other at-risk
individuals and their success odds or ex-convict progress and
success odds stored on a memory device accessible by the one or
more computer processors.
[0059] In some embodiments, the one or more tangible,
non-transitory computer readable media further comprise
instructions that, when executed by the one or more processors,
cause the one or more processors to perform one or more of the
following steps: assigning a mentor candidate to the at-risk
subject, wherein the mentor candidate is selected from a mentor
qualification database on a tangible memory device accessible by
the one or more computer processor; retrieving, via the one or more
computer processors, information about how to reduce risks
associated with one or more of the plurality of risk factors from a
database stored on the one or more tangible memory device;
electronically delivering, via the computer processors, the
information to the oversight board and/or a mentor approved by the
oversight board; receiving, via a user interface of the
application, the at-risk subject's progress report after the
establishment of a mentor-mentee relationship; comparing, via the
one or more processors, the at-risk subject's progress to at-risk
individual success odds or ex-convict success odds stored on a
memory device accessible by the one or more computer processors;
providing, via the one or more processors, a financial incentive to
the mentor based on result of the comparing step; and maintaining,
via the one or more processors, a mentor incentive database,
wherein the mentor incentive database is stored on a tangible
memory device accessible by the one or more processors.
[0060] In some embodiments, the one or more tangible,
non-transitory computer readable media further comprise
instructions that, when executed by the one or more processors,
causes the one or more processors to perform a process using
ontology related artificial intelligence to improve the risk
database functionality.
[0061] In some embodiments, the one or more tangible,
non-transitory computer readable media further comprise
instructions that, when executed by the one or more processors,
cause the one or more processors to perform a process using
artificial intelligence to matches the at-risk candidate with
mentors in the mentor candidate database.
[0062] In some embodiments, the one or more tangible,
non-transitory computer readable media further comprise
instructions that, when executed by the one or more processors,
cause the one or more processors to perform one or more of the
following the steps: obtaining income tax payment information from
relevant government agencies, determining an amount of the cash
payment and/or the bonus (in the form of income tax credit) to the
mentor, obtaining approval from the oversight board, obtaining
approval from relevant governmental agencies (e.g., IRS, state or
city department of taxation) and processing the amount approved by
the governmental agencies for payment.
[0063] In some embodiments, the one or more tangible,
non-transitory computer readable media further comprise
instructions that, when executed by the one or more processors,
cause the one or more processor to perform the step of transmitting
a notification of payment to a department, company or agency that
handles payments to the mentors, wherein the notification causes
the department, company or agency to process payment to the
mentor.
[0064] In some embodiments, the one or more tangible,
non-transitory computer readable media further comprise
instructions that, when executed by the one or more processors,
cause the one or more processors to perform the step of
transmitting an alert of the cash payment to a mentor to a relevant
governmental agency (e.g., IRS) to cause the governmental agency to
issue a reimbursement for the cash payment. In some embodiments,
the method further comprises the step of transmitting an alert of
the issuance of an income tax credit to a mentor to a relevant
governmental agency (e.g., IRS) to cause the governmental agency to
enter the tax credit into the mentor's tax record.
[0065] In some embodiments, the one or more tangible,
non-transitory computer readable media further comprise
instructions that, when executed by the one or more processors,
cause the one or more processors to perform the steps of obtaining
income tax payment information from relevant government agencies.
In some embodiments, the obtaining step described above is replaced
with, or combined with, the step of obtaining the at-risk subject's
projected income tax payments and/or estimated lifetime costs to
society from relevant governmental agencies.
[0066] In some embodiments, the one or more tangible,
non-transitory computer readable media further comprise
instructions that, when executed by the one or more processors,
cause the one or more processors to perform the steps of
determining an amount of the cash payment and/or the bonus (in the
form of income tax credit or cash payment) to the mentor, obtaining
approval from the oversight board, notifying relevant governmental
agency (e.g., IRS, state or city department of taxation) about the
approved amount, wherein the notification causes the relevant
governmental agency to process payment to the mentor and
electronically deliver the payment to a bank account designated by
the mentor.
[0067] In some embodiments, the system further comprises one or
more of the following databases: at-risk individuals' success odds
database, ex-convicts' success odds database, mentors'
qualification database, mentors' performance database and mentors'
compensation database. In some embodiments, the one or more
tangible, non-transitory computer readable media further comprise
instructions that, when executed by the one or more processors,
causes the one or more processors to perform a process using
ontology related artificial intelligence to improve the
functionality of the at-risk individuals' success odds database,
ex-convicts' success odds database, mentors' qualification
database, mentors' performance database and mentors' compensation
database.
[0068] Computer System for Providing Incentives to Mentors of
At-Risk Mentees
[0069] Another aspect of the present application relates to a
computer system for generating and/or maintaining a mentor
incentive database, and providing incentive to mentors of at-risk
mentees. The computer system comprises a computer processor and one
or more tangible, non-transitory computer readable media accessible
by the computer processor, wherein the one or more tangible,
non-transitory computer readable media comprise instructions that,
when executed by the one or more processors, cause the one or more
processors to perform the steps of: determining a mentee's behavior
and progress in a period of time; determining the mentee's income
and income tax payments and/or estimated lifetime costs to society
during the same period of time; and calculating a financial
incentive to the mentee's mentor, wherein the amount of the
financial incentive is calculated based on the mentee's behavior,
or accomplishments (e.g., graduating from high school or attending
or graduating from college), and/or income tax payment and/or
estimated lifetime costs to society during the period of time using
a compensation matrix stored in the one or more tangible computer
readable media.
[0070] In some embodiments, the compensation matrix is generated by
an artificial intelligence process based on a set of initial rules
and criteria. The rules and criteria may be modified from time to
time by the artificial intelligence module based on funding and
feedback from mentors and the users of the computer system.
[0071] In some embodiments, the tangible computer readable medium
comprises instructions stored thereon for selecting mentors and/or
mentees based on selection factors including, in the case of
mentors, their relative career and lifetime success, their
emotional maturity, their job and family status, their ability and
willingness to dedicate the time necessary to be a successful
mentor, among other factors, and in the case of mentees, their age,
family situation, school performance, school attendance, school
dropout rates, gang involvement or temptations, drug use, criminal
activity, and maturity, among other factors. In some embodiments,
the tangible computer readable medium comprises instructions stored
thereon for selecting mentors and/or mentees through an artificial
intelligence process.
[0072] In some embodiments, the tangible computer readable medium
comprises instructions that, when executed by a processor causes
the processor to: (1) receive a selection profile concerning a
potential mentor or mentee candidate, wherein the selection profile
comprises a plurality of selection factors, qualifications of the
mentor and/or risks faced by the mentee; (2) assign a selection
point value to the potential candidate based on the qualifications
of the mentor, the severity level of the potential mentee
candidate's risks and the risk point matrix stored in the memory
device, wherein better scores are higher scores; (3) assign
additional selection point values to the subject based on other
selection factors in the potential candidate's selection profile,
wherein better scores are higher scores; (4) determine a total
selection point value of the potential candidate; and (5a) if the
total candidate point value is equal to or exceeds a predetermined
threshold value, accept the potential candidate as a mentor or
mentee candidate, or (5b) if the total risk point value is below
the predetermined threshold, reject the potential candidate as a
mentor or mentee candidate. In some embodiments, the rating system
in steps (2) and (3) are designed in such a way that better scores
are lower scores, including negative scores, and the potential
candidate is accepted as a mentor or mentee candidate if the total
candidate point value is equal to or below a predetermined
threshold value in step (5a), or is rejected as a mentor or mentee
candidate if the total candidate point value exceeds a
predetermined threshold value in step (5b).
[0073] In some embodiments, the tangible computer readable medium
stores estimated and actual costs to society associated with
children who "fail" to become productive. Examples of such costs
include, but are not limited to, the costs of welfare, food stamps,
unemployment payments, Medicaid, teenage pregnancy, drug use,
crime, prison, lack of income tax payments, failing to graduate
from high school, and a risk point matrix. In some embodiments, the
tangible computer readable medium comprises instructions that, when
executed by a processor, cause the processor to: (1) receive a risk
profile concerning the mentee subject, wherein the risk profile
comprises a plurality of risk factors including the severity level
of the subject's probability of dropping out of school or failing
in a variety of other ways that will be expensive to society in
terms of actual dollars and/or opportunity costs compared to what
the mentee might achieve with appropriate guidance; (2) evaluate
the expected value of the mentee's life, from society's point of
view and compare it to what might be achieved with appropriate
guidance; (3) recommend a compensation factor to be assigned to the
mentee's mentor that will provide a strong incentive to the mentor
while allowing society to retain a significant benefit as well; and
(4) recommend a share of the income taxes that will be paid by the
mentee and that will then be paid to the mentor by the tax
authorities in recognition of mentor's role in ensuring the
mentee's success or recommend a share of the improved expected
value of the mentee's life that will then be paid to the mentor by
government agencies in recognition of the mentor's role in ensuring
the mentee's improved success and lower than expected costs to
society.
[0074] In some embodiments, the one or more tangible,
non-transitory computer readable media store income and income tax
data regarding the mentee and/or the mentee's family in order to:
(1) provide a basis for payments to mentors as compensation for
their services; (2) provide a periodic basis for analysis of
mentee's productivity and success relative to the Success Odds
analysis originally projected based on the mentee's risk assessment
prior to becoming a mentee; and (3) provide a basis for analysis of
mentor's productivity and success relative to the Success Odds
analysis originally projected based on the mentee's risk assessment
prior to becoming a mentee.
[0075] In some embodiments, the payments to mentors is provided in
the form of points payment to the mentor. The tangible,
non-transitory computer readable media further comprises
instructions that, when executed by a processor, cause the
processor to calculate the amount of the points payment as a
percentage of (1) the at-risk subject's income tax payment, and/or
(2) the at-risk subject's contributions to the at-risk subject's
retirement plan and/or education plan, and/or (3) the at-risk
subject's avoidance of negative outcomes and events that results in
costs to society.
[0076] In some embodiments, the tangible, non-transitory computer
readable media further comprises instructions that, when executed
by a processor, cause the processor to obtain federal and/or local
tax (e.g., state and city taxes) payment information from the
at-risk subject, and verify the federal and/or local tax (e.g.,
state and city taxes) payment information with corresponding
governmental agencies. In some embodiments, the tangible,
non-transitory computer readable media further comprises
instructions that, when executed by a processor, causes the
processor to obtain permission from the at-risk subject to access
the at-risk subject's personal income or family income tax payment
information in relevant government agencies.
[0077] In some embodiments, the tangible, non-transitory computer
readable media further comprises instructions that, when executed
by a processor, causes the processor to obtain retirement plan
contribution information and/or educational savings plan
information from the at-risk subject, and verify the retirement
plan contribution and/or educational plan contribution information
with the relevant financial institutions. In some embodiments, the
tangible, non-transitory computer readable media further comprises
instructions that, when executed by a processor, causes the
processor to obtain permission from the at-risk subject to access
the at-risk subject's retirement plan and/or educational plan
information in relevant financial institutions.
[0078] In some embodiments, the tangible, non-transitory computer
readable media stores recidivism rates for ex-convicts and compares
them to recidivism rates for ex-convicts who become mentees and
keeps track of mentor actions designed to help their mentees become
successful and keeps track of mentee income and income tax payments
in order to: (1) provide a basis for payments to mentors whose
mentees avoid future crimes and future prison sentences; (2)
provide a basis for determining which mentor actions and strategies
are most successful for which types of mentees; (3) provide a basis
for determining which mentor qualities and/or qualifications are
most helpful for which types of mentees; (4) assign payments to
mentors reflecting the time period the mentees have avoided
criminal behavior and/or the income tax payments made by the
mentees and/or their costs to society; (5) provide an ongoing
database which can be analyzed in order to assign future mentor
compensation rates based on the success of the mentoring program;
and (6) provide a basis for analyzing the overall costs of
recidivism, in terms of court costs, prison costs, and society's
costs due to the crimes being committed. The higher amounts of
income tax paid by ex-convicts who become mentees may be only a
small fraction of the overall benefit to society that is achieved
with this program. In some embodiments, the tangible,
non-transitory computer readable media stores instructions which,
when executed by the one or more processors, causes the one or more
processors to calculate estimated costs to society, perform
mentees' success odds analysis, and calculate mentors' compensation
using an artificial intelligence process.
[0079] Tangible, Non-Transitory Computer Readable Medium
[0080] Another aspect of the present application relates to a
tangible, non-transitory computer readable medium. The tangible,
non-transitory computer readable medium comprises instructions
stored thereon for providing mentoring services to at-risk people,
the instructions when executed by a processor cause the processor
to perform the steps of: receiving, via a user interface of an
application executing on the computer processor, a risk profile
concerning an at-risk subject, wherein the risk profile comprises a
plurality of risk factors; assigning a risk point value to each of
the plurality of risk factors based on severity level of the
subject's risk factors and a risk point matrix stored on one or
more tangible, non-transitory computer readable media accessible by
the processor; determining a total risk point value of the subject;
and when the total risk point value reaches a predetermined
threshold value, accepting the at-risk subject as a mentee
candidate.
[0081] In some embodiments, the risk point matrix is determined by
evaluating a large scale data base reflecting risks of other
subjects compared to their later successes or failures and costs to
society.
[0082] In some embodiments, the tangible, non-transitory computer
readable medium comprises instructions that, when executed by a
computer processor, causes the computer processor to perform one or
more of the following steps: searching, via computer processor, a
mentor candidate database comprising a plurality of mentor
profiles, wherein each mentor profile comprises personal data,
mentoring experience and a plurality of mentor matching tags;
assigning, via the computer processor, at least one mentor
candidate to each mentee candidate, wherein the at least one mentor
candidate is selected based on a match between the mentee matching
tags and the mentor matching tags, where in the match is performed
using a matching algorithm; submitting the at least one assigned
mentor candidate to an oversight board for approval; receiving, via
the user interface of the application, the at-risk subject's
progress report after the establishment of a mentor-mentee
relationship; comparing, via the computer processor, the at-risk
subject's progress to the progress achieved by other at-risk
individuals and their success odds or ex-convict progress and
success odds stored on a memory device accessible by the computer
processor.
[0083] In some embodiments, the tangible, non-transitory computer
readable medium further comprises instructions that, when executed
by a processor, causes the processor to perform the steps of:
assigning a mentor candidate to the at-risk subject, wherein the
mentor candidate is selected from a mentor qualification database
on a tangible, non-transitory memory device accessible by the
processor; retrieving, via the one or more computer processors,
information about how to reduce risks associated with one or more
of the plurality of risk factors from a database stored on the one
or more tangible memory device; and electronically delivering, via
the computer processors, the information to the oversight board
and/or a mentor approved by the oversight board.
[0084] In some embodiments, the tangible non-transitory computer
readable medium further comprises instructions that, when executed
by a processor, causes the processor to perform the steps of:
receiving, via a user interface of the application, the at-risk
subject's progress reports after the establishment of a
mentor-mentee relationship; comparing, via the one or more
processors, the at-risk subject's progress to at-risk individual
success odds or ex-convict success odds stored on a memory device
accessible by the one or more computer processors; providing, via
the one or more processors, a financial incentive to the mentor
based on result of the comparing step; and maintaining, via the one
or more processors, a mentor incentive database, wherein the mentor
incentive database is stored on a tangible, non-transitory memory
device accessible by the one or more processor.
[0085] In some embodiments, the tangible, non-transitory computer
readable media further comprises instructions that, when executed
by a processor, causes the processor to obtain federal and/or local
tax (e.g., state and city taxes) payment information from the
at-risk subject, and verify the federal and/or local tax (e.g.,
state and city taxes) payment information with corresponding
governmental agencies. In some embodiments, the tangible,
non-transitory computer readable media further comprises
instructions that, when executed by a processor, causes the
processor to obtain permission from the at-risk subject to access
the at-risk subject's personal income or family income tax payment
information in relevant government agencies.
[0086] In some embodiments, the tangible, non-transitory computer
readable media further comprises instructions that, when executed
by a processor, causes the processor to obtain retirement plan
contribution information and/or educational savings plan
information from the at-risk subject, and verify the retirement
plan contribution and/or educational plan contribution information
with the relevant financial institutions. In some embodiments, the
tangible, non-transitory computer readable media further comprises
instructions that, when executed by a processor, causes the
processor to obtain permission from the at-risk subject to access
the at-risk subject's retirement plan and/or educational plan
information in relevant financial institutions.
[0087] FIG. 1 is a block diagram illustrating exemplary hardware
components that may be used for implementing aspects of the systems
and methods for processing information and using the information to
improve matching between mentors and mentees, to improve the
success of relationships between mentors and mentees, and to
provide incentives to mentors. A computer system 100 may include
and execute programs to perform functions described herein,
including steps of method described above. While only one processor
114 is shown in FIG. 1, it is understood that the computer system
100 may include multiple processors. Additionally, the system 100
may include multiple networked computers. Further, a mobile device
that includes some of the same components of computer system 100
may perform steps of the method described above. Computer system
100 may connect with a network 118, e.g., Internet, or other
network, to receive inquires, obtain data, and transmit information
(e.g., to a user work station or other user computing device) as
described above.
[0088] Computer system 100 typically includes a memory 102, a
secondary storage device 112, and a processor 114. Computer system
100 may also include a plurality of processors 114 and be
configured as a plurality of, e.g., bladed servers, or other known
server configurations. Computer system 100 may also include an
input device 116, a display device 110, and an output device
108.
[0089] Memory 102 may include RAM or similar types of memory, and
it may store one or more applications for execution by processor
114. Secondary storage device 112 may include a hard disk drive,
floppy disk drive, CD-ROM drive, or other types of non-volatile
data storage. Processor 114 may include multiple processors or
include one or more multi-core processors. Any type of processor
114 capable of performing the calculations described herein may be
used. Processor 114 may execute the application(s) that are stored
in memory 102 or secondary storage 112, or received from the
Internet or other network 118. The processing by processor 114 may
be implemented in software, such as software modules, for execution
by computers or other machines. These applications preferably
include instructions executable to perform the functions and
methods described above and illustrated in the Figures herein. The
applications may provide graphic user interfaces (GUIs) through
which users may view and interact with the application(s).
[0090] Also, as noted, processor 114 may execute one or more
software applications in order to provide the functions described
in this specification, specifically to execute and perform the
steps and functions in the methods described above. Such methods
and the processing may be implemented in software, such as software
modules, for execution by computers or other machines.
[0091] Input device 116 may include any device for entering
information into computer system 100, such as a touch-screen,
keyboard, mouse, cursor-control device, microphone, digital camera,
video recorder or camcorder. Input device 116 may be used to enter
information into GUIs during performance of the methods described
above. Display device 110 may include any type of device for
presenting visual information such as, for example, a computer
monitor or flat-screen display (or mobile device screen). Output
device 108 may include any type of device for presenting a hard
copy of information, such as a printer, and other types of output
devices, including speakers or any device for providing information
in audio form.
[0092] Examples of computer system 100 include dedicated server
computers, such as bladed servers, personal computers, laptop
computers, notebook computers, palm top computers, network
computers, mobile devices, or any processor-controlled device
capable of executing a web browser or other type of application for
interacting with the system. If computer system 100 is a server,
server 100 may not include input device 116, display device 110 and
output device 108. Rather, server 100 may be connected, e.g.,
through a network connection to a stand-alone work station (another
computer system) that has such devices.
[0093] Although only one computer system 100 is shown in detail,
the computer system 100 may use multiple computer systems or
servers as necessary or desired to support the users, as described
above. Aspects may also use back-up or redundant servers to prevent
network downtime in the event of a failure of a particular server.
In addition, although computer system 100 is depicted with various
components, one skilled in the art will appreciate that the server
can contain additional or different components. In addition,
although aspects of an implementation consistent with the above are
described as being stored in memory, one skilled in the art will
appreciate that these aspects can also be stored on or read from
other types of computer program products or computer-readable
media, such as secondary storage devices, including hard disks,
floppy disks, or CD-ROM, or other forms of RAM or ROM.
Computer-readable media may include instructions for controlling a
computer system, such as the computer system 100, to perform a
particular method, such as methods described above.
[0094] FIG. 2 shows a plurality of databases (DB) that may be
stored in either memory 102, secondary storage 112, or a
combination of memory 102 and secondary storage 112. For purposes
of description only, this description will assume that the
plurality of databases are stored on the secondary storage 112. The
plurality of database may include any suitable database, such as a
document-oriented database, a full-text database, a spatial
database, a distributed database, and a relational database. One of
ordinary skill in the art would readily recognize that other types
of databases may be used. In some embodiments, the databases (DB)
include mentees DB 200 and mentors DB206. The mentees DB 200 may
include a risk DB 201, at-risk individual's success odds DB 202 and
ex-convict success odds DB 204. The mentors' DB 206 may include
mentors' qualifications DB 208, mentors' performances DB210 and
mentors' incentives DB 212
[0095] Risk Database
[0096] The risk DB 201 may include information on a large number of
at-risk people, risk factors associated with each at-risk person,
and follow-up information on each at-risk person. Such information
may be assembled from various data sources, such as school records,
DMV records, police records and correction facility records,
personal medical records, financial transaction records and tax
records. In some embodiments, the risk DB may include a
sub-database reflecting risks of subjects compared to their later
successes or failures and estimated lifetime cost to society. In
some embodiments, the risk DB 201 may further include a
sub-database of methods and devices for risk reduction.
[0097] At-Risk Individual's Success Odds Database
[0098] The at-risk individual success odds DB 202 may store a
plurality of at-risk individuals' records. The at-risk individual
success odds DB 202 may be used to store various attributes or
characteristics about each individual and his or her life situation
stored in DB 202. The various stored attributes or characteristics
may comprise a risk profile for each individual stored in the
children success odds DB 202. For example, the at-risk individual
success odds DB 202 may include attributes or characteristics such
as the individual's IQ (intelligence quotient), the individual's
prior success in school (attendance rates, grades, teacher
assessments, discipline problems, etc.), the individual's family
income, the neighborhood or ZIP code of where the individual lives,
the school the individual attends, the dropout rate of the school
the individual attends, the family status of the individual's
family, the success of the individual's siblings, the individual's
neighborhood's crime rates, the individual's neighborhood's gang
activity, and the individual's neighborhood's drug use. In some
embodiments, the Success Odds (SO) are calculated based on the
following formula:
SO=f(a,b,c,d,e,f,g,h,i,j)=Aa+Bb+Cc+Dd+Ee+Ff+Gg+Hh+Ij+Jj+Kk+Ll
[0099] wherein a=IQ, b=family income, c=neighborhood assessment,
d=school assessment, e=family status, f=sibling success, g=crime
rate, h=local drug use, i=dropout rate, j=school success,
k=neighborhood gang activity, l=other factors, and wherein the
weight of each contributing factor may be modified by a modifying
factor (e.g., A, B, C, D, E, F, G, H, I, J, K, or L).
[0100] One of ordinary skill in the art would readily recognize
that other attributes or characteristics may be stored in the
at-risk individual success odds DB 202. Based on these attributes
or characteristics, the system 100 may generate a risk point value
for each individual using a probability of various life events. For
purposes of description only, the remainder of this disclosure will
assume that the higher the risk point value, the more likely the
individual is at-risk. One of ordinary skill in the art would
readily recognize that the risk point value may be defined in such
a manner that, the lower the risk point value, the more likely the
individual is, at-risk. For example, the various life events may
include graduating from high school, graduating from college,
becoming a contributing member of society, future drug use, future
criminal conviction, future unemployment, future welfare
assistance, or future premature death. One of ordinary skill in the
art would readily recognize that other life events may be
calculated using the attributes or characteristics about each
individual stored in the at-risk individual's success odds DB 202.
To generate the risk point value for each individual, the system
100 may assign a certain number of points for each of the life
events described above. The system 100 may then simply add all of
the points to generate the risk point value. Alternatively, the
system 100 may assign a weighting factor to each of the life
events. The system 100 may then generate the risk point value using
weighting factors and the points for each life event described
above. Likewise, there may be attributes which correlate in a way
that affects the risks for some at-risk individuals. In this case,
for example, an individual whose siblings all did well may not be
affected by gang activity or drug use in the neighborhood; however,
if siblings were susceptible to such dangers, then the weight of
these risks would be magnified. So the simplistic formula above may
become more complicated as analysis of the available data
demonstrates interrelationships of risk factors that need to be
evaluated by the computer system.
[0101] In another aspect of this disclosure, the system 100 may add
to the risk point value that was calculated based on the life
events described above. For example, the system 100 may factor in
the gender, the individual's non-academic interests, the section of
the country in which the individual lives, and the individual's
overall appearance. One of ordinary skill in the art would readily
recognize that other non-life events may be factored in by the
system 100. A certain number of risk points may be assigned to
these non-life events. These risk points may then be added to the
risk points based on life events. Alternatively, these risk points
may also be weighted and then added to the risk points based on
life events. In some embodiments, the at-risk individual's success
odds DB contains a sub-database for at-risk children.
[0102] Ex-Convict's Success Odds Database
[0103] In one aspect of this disclosure, a second database may be
an ex-convict success odds DB 204. The ex-convict success odds DB
204 may function similarly to the at-risk individual's success odds
DB 202 as described above with the exception that the DB is for
ex-convicts rather than at-risk individuals in general. The
ex-convict success odds DB 204 may be used to store various
attributes or characteristics about each ex-convict stored in the
DB 204. The various stored attributes or characteristics may
comprise a risk profile for each ex-convict stored in the
ex-convict success odds DB 204. For example, the ex-convict success
odds DB 204 may include attributes or characteristics such as the
ex-convict' s committed crime, the number of years in prison, the
ex-convict's education level, the ex-convict's workplace skills,
the ex-convict's family support system, the ex-convict's
personality, which may be assessed by a trained professional, the
ex-convict's drug use history, the ex-convict's gang involvement
history, the ex-convict' s behavior while in prison, and other
factors that may be used by evaluating historical recidivism data.
Based on these attributes or characteristics, the system 100 may
generate a risk point value for each ex-convict. For purposes of
description only, the remainder of this disclosure will assume that
the higher the risk point value, the more likely the ex-convict is
at-risk. One of ordinary skill in the art would also readily
recognize that the risk point value may be defined in such a manner
that, the lower the risk point value, the more likely the
ex-convict is at-risk. One of ordinary skill in the art would also
readily recognize that other attributes or characteristics may be
stored in the ex-convict success odds DB 204. To generate the risk
point value for each ex-convict, the system 100 may assign a
certain number of points for each of the attributes or
characteristics described above. The system 100 may then simply add
all of the points to generate the risk point value. Alternatively,
the system 100 may assign a weighting factor each of the attributes
or characteristics. The system 100 may then generate the risk point
value using weighting factors and the points for each attribute or
characteristic described above. In some embodiments, this analysis
is completed while the individual is still in prison.
[0104] In some embodiments, the Success Odds of ex-convicts
(SO.sub.ExCon) are evaluated based on the following formula:
(SO.sub.ExCon)=f(a,b,c,d,e,f,g,h,i)=Aa+Bb+Cc+Dd+Ee+Ff+Gg+Hh+Ii+Jj
[0105] Wherein a=crime committed, b=years in prison, c=education,
d=workplace skills, e=family support system, f=personality
assessment by a trained professional, g=drug use history, h=gang
involvement history, i=behavior while in prison, j=other factors
found by evaluating historical recidivism data, and wherein the
weight of each contributing factor may be modified by a modifying
factor (e.g., A, B, C, D, E, F, G, H, I or J). As with the formula
for the at-risk individuals, the equation above may need to be
significantly more complicated if it is determined that various
factors are interrelated in their effects.
[0106] If the Success Odds are below a certain level, then clearly
intervention by a mentor could be very valuable. As the
mentor-mentee relationship continues, data could also be collected
regarding the impact of mentors on various "types" of at-risk
individual, for example the impact of mentoring on the at-risk
individuals with Success Odds of 20-30% vs. the impact of mentoring
on the at-risk individuals with Success Odds of 0-10%. In some
embodiments, the amount of mentor incentive varies depending on the
magnitude of the challenge that the mentor will face in helping his
or her mentee to succeed. In some embodiments, mentors are assigned
to mentees of the same gender. In some embodiments, mentors are
assigned to mentees of different gender. In some embodiments,
mentors are assigned to mentees of the same ethnicity. In other
embodiments, mentors are assigned to mentees of different
ethnicity. Other factors to be considered for mentor/mentee pairing
include regularity of interaction, geographic distance, the family
situation of the mentor, the job status of the mentor, etc. In some
embodiments, the system analyzes periodically the past
mentor/mentee pairing data and results and determines what seems to
be working and what seems to be failing. The knowledge accumulated
in the analysis is used to improve future pairings as well as to
advise current mentors and mentees about which behavior
characteristics they should consider employing for best
results.
[0107] The risk of recidivism for ex-convicts can be calculated in
a way similar to the Success Odds of the at-risk individuals. In
some embodiments, the mentor-mentee relationship is established
with an in-prison mentor while an ex-con mentee is in prison and
the mentoring continues with an outside mentor after the mentee is
released from the prison. In some embodiments, the in-prison mentor
is selected from people who work in prison and the outside mentor
is selected from people who work outside of prison. In some
embodiments, the in-prison mentors are paid based on the recidivism
rates of the ex-con mentees and the outside mentors are paid based
on the ex-con mentees' income tax payments. In some embodiments,
the in-prison mentor and the outside mentor is the same person.
[0108] Mentors' Databases
[0109] In one aspect of this disclosure, a third database may be
the mentors' DB 206. The mentors' DB 206 may be comprised of a
plurality of databases. The mentors' DB may be comprised of, for
example, a mentor's qualifications DB 208, a mentor's performances
DB 210, and a mentor's incentives DB 212. One of ordinary skill in
the art would readily recognize that more or fewer databases may be
used.
[0110] The mentors' qualifications DB 208 may be used to store a
plurality of mentor records. For example, the mentors'
qualifications DB 208 may store various attributes or
characteristics about each mentor stored in mentors' qualifications
DB 208. For example, the mentors' qualifications DB 206 may include
attributes or characteristics such as each mentor's education,
profession, job history, criminal history, health history, drug use
history, leadership roles or positions, family status, or any other
attributes or characteristics that may be helpful in being a
successful mentor. The mentors' qualifications may also include
training classes attended, relevant books read, or tests passed all
of which may be relevant in preparing a mentor for success. A
mentor profile may be generated for each mentor using these
attributes or characteristics. One of ordinary skill in the art
would readily recognize that other attributes or characteristics
may be used when generating the mentor profiles. There may also be
"free-form" entries, such as leadership positions held within the
mentor's community or letters of recommendations or references
provided by the mentor attesting to, for example, the mentor's
character. The mentors' qualifications may be periodically updated
to account for, for example, new references or leadership
positions. Longer term, each mentor's track record of success or
failure with his or her mentees will also be an important factor in
judging the mentor's qualifications in the future. Keeping track of
each of these elements on a massive scale should allow the ability
over time to correlate which seem to have an effect on overall
mentoring success.
[0111] The mentors' qualifications DB 208 may also store
potentially disqualifying attributes or characteristics. For
example, if a potential mentor volunteers for the system 100, then
the potential mentor may be added to the mentor DB 206. However, if
it is later found out that the potential mentor is, for example, an
alcoholic, a drug user, a criminal or a child molester, the
potential mentor would then be disqualified.
[0112] In addition to qualifications, the mentors' qualifications
DB 208 may also include suitability measures for a given
mentor-mentee pair. For example, before a mentor is assigned to a
mentee, the mentee and mentor may audition each other for, for
example, compatibility. The mentors' qualifications DB 208 and the
at-risk individual's success odds DB 202 may also store the results
of such an audition. Moreover, the mentee may reject a mentor after
a mentor-mentee relationship has been established. In such a case,
the mentors' qualifications DB 208 may include a note indicating
that the mentor is not suitable for the mentee. Depending on the
contents of that note, the mentor may not be considered suitable
for any future mentee. Depending on the timing of that note and the
reasons behind it, the mentor's right to any future payments may be
eliminated or adjusted.
[0113] The mentors' performances DB 210 may also be used to store a
plurality of mentor records. For example, the mentors' performances
DB 210 may store records related to how well the mentors are
performing. One way to assess how well the mentors are performing
is by comparing results of the mentees to the at-risk individuals'
success odds or the ex-convicts' success odds. The greater the
at-risk individuals or ex-convicts are succeeding compared to the
respective success odds, the better the mentors are performing.
Another way the system 100 may assess how well the mentors are
performing is by including reports from various parties. For
example, the mentee's parents, teachers, or other interested
parties may provide reports discussing in what ways the mentor is
having a positive or negative impact on the mentee. One of ordinary
skill in the art would readily recognize that other performance
metrics may be used to determine the efficacy of the mentor. The
performance metrics may be collected periodically. In some
embodiments, various alerts are provided by the system if results
are significantly positive or negative so that appropriate action
can be taken to either duplicate or eliminate the behavior
reported.
[0114] Additionally, the mentors' performances DB 210 may include a
plurality of problem-solution pairs. For example, if a mentor
reports that a certain solution worked well for a mentee in a given
situation, this may be noted in the mentors' performances DB 210.
Alternatively, if a mentor reports that a certain solution did not
work well for a mentee in a given situation, this also may be noted
in the mentors' performances DB 210. Such problem-solution pairs
may be useful for other mentor-mentee relationships.
[0115] In some embodiments, the system 100 may include an
artificial intelligence module that is utilized to improve the
functionality of the ex-convict success odds DB 204, the mentor DB
206 and any other databases in the system 100. In some embodiments,
the artificial intelligence module is also utilized to select
mentor for a mentee.
[0116] The system 100 may also include an oversight board to review
and approve a mentor-mentee pair, and to track how well the mentors
are performing. The board may use the information in the mentors'
performances DB 210 to perform the tracking. In addition to
tracking how well the mentors are performing, the board may assign
a mentor to a mentee. The assignment may take place based on, for
example, mentee success odds, mentor qualifications, and mentor
performances. Additionally, the board may limit the number of
mentees a mentor may have. For example, if a mentor is new and has
not yet proven that he is a good mentor, the board may limit the
number of mentees the mentor may have at any given time initially.
If the mentor performs well, then the board may increase the number
of mentees the mentor may have. In any case, it is likely that the
allowable number of mentees should grow for each mentor as the
mentor demonstrates success and as mentees perhaps require fewer
hours per week as they mature and succeed with their lives. In some
embodiments, the oversight board is also responsible for reviewing
and approving mentor's compensation.
[0117] In some embodiments, the system 100 may retrieve information
about the qualifications of potential mentors in general and/or
their specific qualifications with respect to mentoring a specific
mentee candidate or type of mentee candidate, and electronically or
otherwise deliver the information to the oversight board which will
be responsible for approving the assignment of a mentor to the
particular mentee. The system 100 may also electronically or
otherwise deliver some aspects of the mentor information to
potential mentees or their parents or guardians and obtain a
response from the potential mentees about their willingness to work
with a particular mentor.
[0118] In some embodiments, the system 100 evaluates or provides
information to the oversight board to evaluate the probability that
the potential mentor will be successful in reducing the various
risk factors associated with one or more potential mentees. Factors
to be analyzed will include their education, family status, age,
non-work interests, jobs held, criminal history, health, drug use,
leadership, ethnicity, religion, or other personality trait track
records. Their position in the community and references from
respectable people testifying to the qualifications of the mentors
could also be important. By tracking mentors' qualifications and
personal and professional attributes compared to their performance
over time, the data will become available to provide future
guidance about which potential mentors would be most effective.
Correlating these results with the attributes of their respective
mentees could also be productive. The best mentor for person X
might be far different than the best mentor for person Y.
[0119] In some embodiments, the system 100 electronically tracks
mentors' performance in guiding their mentees. These could include
statistics about the success of the mentees relative to their
initial Success Odds and could include reports from the mentees
and/or their parents or teachers or other interested parties.
Whether data should be collected weekly or monthly or in some other
periodic fashion will also be influenced by an ongoing analysis of
the data. Higher frequency early in the relationship will certainly
make sense, but the time interval may be extended based on stable
positive relationships and progress. In some embodiments, data are
collected weekly in the first 1, 2, 3, 4, 5 or 6 months in the
mentor-mentee relationship. The time interval for data collection
is extended to 2 weeks, 1, 2, 3 or 6 months based on the progress
of the mentor-mentee relationship. Of course, learning both the
good and the bad aspects of each relationship can be equally
important. In some cases, even the mentors may need mentors if
particularly challenging situations arise. Mentors are encouraged
to reach out for assistance if they are facing challenging issues.
The system 100 will provide the option for the mentor to read
proposed advice or access to a live person for a brief conversation
or attend a class to learn about the issue or request an expert to
participate in a future mentee meeting. In some embodiments, each
mentee has his/her own database. Having a large scale database that
addresses a wide range of possible problems and solutions will be
critical in order to get the best results for each mentee on a
timely basis. Having a specific database for each mentee will also
be important in order to ensure appropriate progress is being made
and in order to alert authorities if mentor or mentee behavior
appears to be inappropriate or unsuccessful in any way or evidence
suggests that the current mentor/mentee relationship needs to be
terminated or modified in some way. In some embodiments, the system
100 electronically tracks the overall results of the mentoring
process.
[0120] The mentors' incentives DB 212 may be used to store a
plurality of mentor records. The mentor records stored in the
mentors' qualifications DB 208, the mentors' performances DB 210,
and the mentors' incentives DB 212 may all be identical. The system
100 may incentivize mentors for their efforts. For example, the
mentor may receive a portion of his mentee's tax payments. In this
way, the mentor and even the mentor's family and friends may be
incentivized to maximize the financial well-being of his mentee.
There may be some adjustments to this incentive, however. For
example, women generally have lower incomes than men. Accordingly,
there may be an adjustment factor to correct for such income
inequalities. Alternatively, or additionally, the mentor may be
compensated based on the mentee's household tax payments. For
example, the mentee could be woman who becomes a successful but
non-working mother. The mentor may have had a large part to play in
that success. However, since the woman is non-working, she does not
generate any taxable income. Therefore, sharing in the tax payments
based on household income may be a way to appropriately compensate
the mentor.
[0121] The amount of the financial incentive to be provided to the
mentor may reflect the initial estimates of the risk level faced by
the mentee as determined by comparing the mentee's risk factors to
those of the historical data base accumulated in the computer
system and assessing the likely future performance of the mentee
based on those relative risk comparisons. The potential costs of
crime, prison, welfare, and estimated cost to society for some
people with those risk factors are balanced against the positive
impact of those who succeed despite the risk factors. Depending on
how severe the risk factors may be, the share of the future income
tax payments could be very high while still providing a long term
benefit for society if the mentor is successful. In some
embodiments, the amount of the mentor's incentive is calculated as
a percentage of the mentee's personal income tax payment, the
mentee's family income tax payment, the mentee's projected income
tax payment, the mentee's projected family income tax payment, the
mentee's retirement plan contributions and/or the mentee's
education plan contributions. The tax payment may include the
federal tax payment, state tax payment and/or city tax payment.
[0122] The system 100 may also provide for additional bonuses.
These additional bonuses may be based, for example, on specific
goals, such as graduating from high school, achieving a specific
grade point average, gaining acceptance at a college, avoiding teen
pregnancy, drug use, gang activity, or crime. Such goals may not
result in any taxable income. Therefore, one way to compensate the
mentor may be a tax deduction. The tax deduction may depend on how
well the mentee is doing in regard to the specific goal. This could
be, for example, a deduction on mentor's tax bills upon their
mentee reaching a certain age without having succumbed to any of
these temptations or for having achieved some of these goals. In
some embodiments, a database is constructed that tracked the
performance of the mentees on these and other key factors. In some
embodiments, the bonus is provided as an income tax credit to the
mentor, wherein the amount of the income tax credit is calculated
based on the at-risk subject's behavior and/or the at-risk
subject's income tax payment, the at-risk subject's retirement plan
contributions and/or the at-risk subject's education plan
contributions. In some embodiments, the tax credit is a federal
income tax credit, state income tax credit, city income tax credit,
or combinations thereof. In some embodiments, the bonus is a direct
payment to the mentor, which may or may not be considered taxable
income. In some embodiments, the bonus is a direct payment or a
payment of Points based on the mentee's estimated costs to society
compared to the mentee's forecasted costs to society for the mentee
over some period of time. These points may be provided by one or
more corporations or other entities which desire to support the
mentoring activities and promote use of their products and/or
services. In some embodiments, the bonus is a direct payment or
payment of Points based on the difference between the mentee's
estimated costs to society at the beginning of a period of time and
the mentee's estimated costs to society at the end of a period of
time. In some embodiments, the system 100 calculates the projected
income tax payment of the mentee based on verified income
information provided by the mentee.
[0123] In some embodiments, the system 100 electronically obtains
income tax payment information, retirement plan contribution
information and/or education plan contribution information from
relevant government agencies and/or financial institutions,
determines an amount of the cash payment and/or the bonus (in the
form of income tax credit) to the mentor based on the income tax
payment information, retirement plan contribution information
and/or education plan contribution information, and obtains
approval from the oversight board and/or the relevant governmental
agencies (e.g., IRS, state or city department of taxation).
[0124] In some embodiments, the system 100 processes the amount
approved by the governmental agency for payment to the mentor. In
some embodiments, the system 100 transmits a notification of
payment to a department, company or agency that handles payments to
the mentors, wherein the notification causes the department,
company or agency to process payment to the mentor. In some
embodiments, the system 100 further transmits an alert of the cash
payment to a mentor to a relevant governmental agency (e.g., IRS)
to cause the governmental agency to issue a reimbursement for the
cash payment.
[0125] In some embodiments, the system 100 transmits an alert of
the issuance of an income tax credit to a mentor to a relevant
governmental agency (e.g., IRS) to cause the governmental agency to
enter the tax credit into the mentor's tax record.
[0126] In some embodiments, the system 100 obtains income tax
payment information, retirement plan contribution information
and/or education plan contribution information from relevant
government agencies or financial institutions, determines an amount
of the cash payment to the mentor, obtains approval from the
oversight board, notifies relevant governmental agencies (e.g.,
IRS, state or city department of taxation) about the approved
amount, wherein the notification causes the relevant governmental
agency to process payment to the mentor and electronically deliver
the payment to a bank account designated by the mentor.
[0127] Additionally, a convict who is about to be released from
prison or an ex-convict who has already been released from prison
may have multiple mentors, such as two. For example, one mentor may
have worked with the mentee when he or she was inside the prison
and another mentor may be outside the prison. The two mentors may
be compensated differently. For example, the mentor inside the
prison may be compensated based on the mentee's recidivism. The
mentor outside the prison may be compensated based on the mentee's
income tax payments and/or avoidance of costs to society (which
could also include recidivism).
[0128] The initial database describing the Success Odds and the
mentor share of future payments will be very important in focusing
the work while properly motivating the mentors. In some
embodiments, the system 100 tracks the both the mentees and the
mentors in order to determine the success of the relationship and
identifies the key elements of the success or lack thereof. Any
particular mentor may look good on paper, but only time and the
database will be able to determine the true efficacy of his or her
activities. Both future mentors and mentees may have an opportunity
to learn from the success and failures of their predecessors if
they are captured properly in the computer system and analyzed
carefully.
[0129] Mentee Safety and Program Oversight
[0130] It will be crucial to ensure that vigilant oversight of this
process is in place. When launched on a massive scale, care must be
taken to deal with the fact that child molesters, criminals or
simply ineffective mentors may find their way into the system.
Accordingly, in some embodiments, a database and/or emergency
information system is constructed so that any improper behavior can
be instantly reported and dealt with effectively. The contracts
signed with mentors may include clauses that eliminate their right
to future payments if improper behavior occurs. Likewise, mentees
are provided with the right to audition mentors and/or reject them
down the road if they are not comfortable that the relationship is
appropriate or productive for them. Keeping a careful database of
mentor candidates that includes reports of their success and
failures will be critical to ensure that mentees are both protected
and given the best odds of future success. In some embodiments, the
database would be a nationwide database to ensure that "bad apples"
identified in one jurisdiction do not later take root in another.
Likewise, it will no doubt be true that some mentors will develop
spectacular ideas that should be quickly copied across the land.
Collecting and sharing the bad and the good stories from this
database will both be extremely valuable.
[0131] In some embodiments, another associated database can track
the overall mentor review process. An oversight entity may oversee
the mentors' behavior and approve each assignment and review the
success of the assignment on an ongoing basis. One can imagine
various types of misbehavior that could take place in this sort of
bureaucracy, so it will be important to track various metrics to
ensure the best possible results while encouraging whistle blowers
or contrary points of view that may, upon inspection, have great
merit. Having an extensive database that is carefully mined on a
regular basis will help to ensure that the process gets the best
results. Keeping track of drug use, crime, employment rates,
graduation rates, dropout rates, teen pregnancy, and other measures
of success and failure will be important in assessing the ongoing
qualifications of the various mentors. In some embodiments, a new
mentor is limited to just 1 or 2 or a few mentees until he or she
can establish his or her credentials through the success of the
mentees.
[0132] FIG. 3 is a flowchart showing a method 300 to carry out the
system 100, according to one aspect of this disclosure. The method
300 may start at block 302. At block 302, the system 100 may
collect data for mentees and mentors, as well as other relevant
data. At block 304, the system 100 processes the collected data.
The collected data may also be used to populate the mentees DB 200
and the mentor DB 206, for example. The system 100 may generate
mentees' success odds and mentors profiles in block 304. After
block 304 is complete, the method 300 may proceed to block 306 to
start the matching process.
[0133] At block 306, the system 100 may match the mentors with the
mentees. The matching may be carried out entirely by the system
100, entirely by the oversight board described above, or by a
combination of the system and the oversight board. After block 306
is complete, the method 300 may proceed to block 308.
[0134] In some embodiments, the system 100 comprises a matching
module that includes software and/or logic for matching a mentor
with a mentee based on the mentee's profile and the mentor's
profile. In some embodiments, the mentee's profile includes the
mentee's personal information, such as age and gender, the mentee's
risk profile, the mentee's success odds and tags associated with
the mentee, such as location tag (e.g., New York City), language
tag (e.g., Spanish speaking) and behavior tag (e.g., aggressive
behavior). The mentor's profile includes the mentor's personal
information, the mentor's work history, and tags associated with
the mentor, such as location tag (e.g., New York City), language
tag (e.g., Spanish speaking) and experience tag (e.g., experience
in handling aggressive behavior). The matching module may analyze
the tags associated with the mentee's profile and the mentor's
profile, and determine the best match based on a matching
algorithm. In some embodiments, the system 100 comprises an
artificial intelligence module to improve the functionality of
various databases and the mentor-mentee matching process.
[0135] At block 308, the system 100 may track the success of each
mentor-mentee relationship. The system 100 may track the success of
the relationships as described above in reference to FIG. 2. After
block 308 is complete, the method 300 may proceed to block 310.
[0136] At block 310, the system 100 may compensate the mentor based
on how successful the mentor-mentee relationship is. The system 100
may gauge the success of the relationships as described above in
reference to FIG. 2. After block 310 is complete (which may take
years or even decades), the method 300 may end.
[0137] Each of the processes, methods, and algorithms described in
the preceding sections may be embodied in, and fully or partially
automated by, code instructions executed by one or more computer
systems or computer processors comprising computer hardware. The
processes and algorithms may be implemented partially or wholly in
application-specific circuitry.
[0138] The various features and processes described above may be
used independently of one another, or may be combined in various
ways. All possible combinations and subcombinations are intended to
fall within the scope of this disclosure. In addition, certain
method or process blocks may be omitted in some implementations.
The methods and processes described herein are also not limited to
any particular sequence, and the blocks or states relating thereto
can be performed in other sequences that are appropriate. For
example, described blocks or states may be performed in an order
other than that specifically disclosed, or multiple blocks or
states may be combined in a single block or state. The example
blocks or states may be performed in serial, in parallel, or in
some other manner. Blocks or states may be added to or removed from
the disclosed example embodiments. The example systems and
components described herein may be configured differently than
described. For example, elements may be added to, removed from, or
rearranged compared to the disclosed example embodiments.
[0139] Conditional language, such as, among others, "can," "could,"
"might," or "may," unless specifically stated otherwise, or
otherwise understood within the context as used, is generally
intended to convey that certain embodiments include, while other
embodiments do not include, certain features, elements and/or
steps. Thus, such conditional language is not generally intended to
imply that features, elements and/or steps are in any way required
for one or more embodiments or that one or more embodiments
necessarily include logic for deciding, with or without user input
or prompting, whether these features, elements and/or steps are
included or are to be performed in any particular embodiment
[0140] The above description is for the purpose of teaching the
person of ordinary skill in the art how to practice the object of
the present application, and it is not intended to detail all those
obvious modifications and variations of it which will become
apparent to the skilled worker upon reading the description. It is
intended, however, that all such obvious modifications and
variations be included within the scope of the present application,
which is defined by the following claims. The aspects and
embodiments are intended to cover the components and steps in any
sequence which is effective to meet the objectives there intended,
unless the context specifically indicates the contrary.
* * * * *