U.S. patent application number 16/603193 was filed with the patent office on 2020-04-16 for distributed network for the secured collection, analysis, and sharing of data across platforms.
The applicant listed for this patent is Akili Interactive Labs, Inc.. Invention is credited to Mae-ellen Gavin, H. LeRoux Jooste, Matthew Omernick, Jeffrey Steinmetz, Kristin Zibell.
Application Number | 20200118686 16/603193 |
Document ID | / |
Family ID | 63712685 |
Filed Date | 2020-04-16 |
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United States Patent
Application |
20200118686 |
Kind Code |
A1 |
Jooste; H. LeRoux ; et
al. |
April 16, 2020 |
DISTRIBUTED NETWORK FOR THE SECURED COLLECTION, ANALYSIS, AND
SHARING OF DATA ACROSS PLATFORMS
Abstract
Computer-implemented methods and systems for managing the
collection of and access to behavior assessment data. In an
embodiment, a first user having authority to act on behalf of an
individual under study identifies a second and third user role,
specifies behavior data, symptom measurement data, and/or medicine
regimen data associated with the individual under study, and
defines access permissions for the second and third user roles with
respect to the behavior data and symptom measurement data. The
symptoms and behaviors to be measured are specified based on a
condition of the individual. Users provide behavior data and
symptom measurement data observed from the individual. An analytics
module performs computational analysis on the behavior data and
symptom measurement data, thereby producing behavior assessment
data. A reporting module presents the behavior assessment data to
the users in a manner consistent with the defined access
permissions.
Inventors: |
Jooste; H. LeRoux; (Arden,
NC) ; Gavin; Mae-ellen; (Arlington, MA) ;
Zibell; Kristin; (Boston, MA) ; Omernick;
Matthew; (Larkspur, CA) ; Steinmetz; Jeffrey;
(San Francisco, CA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Akili Interactive Labs, Inc. |
Boston |
MA |
US |
|
|
Family ID: |
63712685 |
Appl. No.: |
16/603193 |
Filed: |
April 6, 2018 |
PCT Filed: |
April 6, 2018 |
PCT NO: |
PCT/US2018/026520 |
371 Date: |
October 4, 2019 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
|
62482648 |
Apr 6, 2017 |
|
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G16H 10/60 20180101;
G16H 50/20 20180101; G16H 50/70 20180101; G16H 50/30 20180101; G16H
40/20 20180101; G16H 20/10 20180101 |
International
Class: |
G16H 50/30 20060101
G16H050/30; G16H 10/60 20060101 G16H010/60; G16H 20/10 20060101
G16H020/10; G16H 40/20 20060101 G16H040/20; G16H 50/20 20060101
G16H050/20; G16H 50/70 20060101 G16H050/70 |
Claims
1. A system for managing the collection of and access to behavior
assessment data, the system comprising: one or more processors; and
a memory coupled with the one or more processors; wherein the one
or more processors execute a plurality of modules stored in the
memory, and wherein the plurality of modules comprises: a graphical
user interface at which: a first user having authority to act on
behalf of an individual under study identifies a second user role
and a third user role, specifies one or more of behavior data,
symptom measurement data, and medicine regimen data associated with
the individual under study, and defines access permissions for the
second and third user roles with respect to the behavior data and
symptom measurement data, wherein the symptoms and behaviors to be
measured are specified based on a condition of the individual under
study; and users provide behavior data and symptom measurement data
observed from the individual under study according to either a
second user role or third user role based at least in part on the
access permissions specified by the first user; an authentication
module comprising computer-executable instructions for enforcing
the access permissions such that the second user role is limited to
providing and accessing a first subset of the behavior data and
symptom measurement data and the third user role is limited to
providing and accessing a second subset of the behavior data and
symptom measurement data; an analytics module comprising
computer-executable instructions for performing computational
analysis on the behavior data and symptom measurement data, thereby
producing behavior assessment data; and a reporting module for
presenting the behavior assessment data to the first user, second
user, and third user in a manner consistent with the defined access
permissions specified by the first user.
2. The system of claim 1, wherein the individual is the first
user.
3. The system of claim 1, wherein the analytics module further
applies a classifier model to the provided behavior data and
symptom measurement data to create a composite profile.
4. The system of claim 3, wherein the classifier model comprises at
least one of linear/logistic regression, principal component
analysis, generalized linear mixed models, random decision forests,
support vector machines, or artificial neural networks.
5. The system of claim 3, wherein the classifier model identifies a
correlation between (i) the provided behavior data and symptom
measurement data and (ii) data collected in connection with
individuals who have exhibited desirable treatment response
times.
6. The system of claim 5, wherein the correlation identifies at
least one of an effective intervention, treatment efficacy, and
drug performance.
7. The system of claim 3, wherein the classifier model identifies
an impairment in the individual, said impairment not currently
being treated.
8. The system of claim 1, wherein the analytics module further
applies a classifier model for classifying the individual with
respect to a likelihood of at least one of an onset or a
progression of the condition.
9. The system of claim 1, wherein the behavior assessment data
supports a formulation of a course of treatment or modification of
a course of treatment.
10. The system of claim 1, further comprising a usage analytics
database for storing and providing usage analytics data to the
analytics module.
11. The system of claim 1, further comprising an accounts and
profiles database for storing and transmitting user account and
profile data to the authentication module.
12. The system of claim 1, further comprising a preferences
database for storing and transmitting user preferences to the
authentication module.
13. The system of claim 1, further comprising a health database for
storing and transmitting health information data to the reporting
module.
14. The system of claim 1, wherein the symptom data comprises
performance metric data generated based on the individual's
interactions with a cognitive platform.
15. The system of claim 1, further comprising a content module
configured to generate one or more content queries based at least
in part on the behavior assessment data.
16. The system of claim 14, wherein the content module is further
configured to: submit the one or more content queries to at least
one content library comprising a content index; and analyze content
received from the at least one content library to determine a
relevance to a status of the individual determined based on the
behavior assessment data.
17. A computer-implemented method for managing the collection of
and access to behavior assessment data, the method comprising:
using one or more processors to execute instructions stored in one
or more memory storage devices comprising computer executable
instructions to perform operations including: receiving
instructions from a first user having authority to act on behalf of
an individual under study, the instructions including the
identification of a second user role and a third user role,
specification of one or more of behavior data and symptom
measurement data associated with the individual under study, and
definition of access permissions for the second and third user
roles with respect to the behavior data and symptom measurement
data, wherein the symptoms and behaviors to be measured are
specified based on a condition of the individual under study;
receiving from users behavior data and symptom measurement data
observed from the individual under study according to either a
second user role or third user role based at least in part on the
access permissions specified by the first user; enforcing the
access permissions such that the second user role is limited to
providing and accessing a first subset of the behavior data and
symptom measurement data and the third user role is limited to
providing and accessing a second subset of the behavior data and
symptom measurement data; performing computational analysis on the
behavior data and symptom measurement data, thereby producing
behavior assessment data; and presenting the behavior assessment
data to the first user, second user, and third user in a manner
consistent with the defined access permissions specified by the
first user.
18. The computer-implemented method of claim 17, wherein the
individual is a child.
19. The computer-implemented method of claim 17, wherein the
individual is an adult.
20. The computer-implemented method of claim 17, wherein the
individual is the first user.
21. The computer-implemented method of claim 17, wherein at least a
portion of the behavior data comprises measurements of a behavior
based on diagnostic and symptom criteria for the condition.
22. The computer-implemented method of claim 17, wherein the
behavior data comprises at least one of homework assignment
completion, frequency and quality of performing chores, and quality
of getting along with a person acting on behalf of the
individual.
23. The computer-implemented method of claim 17, wherein at least a
portion of the symptom measurement data comprises measurements of a
symptom on a clinically validated symptom list for the
condition.
24. The computer-implemented method of claim 17, wherein the
symptom measurement data comprises physiological data.
25. The computer-implemented method of claim 24, wherein the
physiological data comprises at least one of electrical activity,
heart rate, blood flow, and oxygenation levels.
26. The computer-implemented method of claim 17, wherein the first
user is a parent of the individual.
27. The computer-implemented method of claim 17, wherein the second
user is a teacher of the individual.
28. The computer-implemented method of claim 17, wherein the third
user is a practitioner treating the individual.
29. The computer-implemented method of claim 17, wherein the
behavior assessment data comprises (i) pace of response of the
individual to a treatment, (ii) a likelihood of onset and/or stage
of progression of the condition, (iii) efficacy of medication at
controlling a behavior, (iv) efficacy of medication at addressing a
symptom of the condition.
30. The computer-implemented method of claim 17, wherein performing
computational analysis comprises applying a classifier model to the
behavior data and symptom measurement data to create a composite
profile.
31. The computer-implemented method of claim 30, wherein the
classifier model comprises at least one of linear/logistic
regression, principal component analysis, generalized linear mixed
models, random decision forests, support vector machines, or
artificial neural networks.
32. The computer-implemented method of claim 30, wherein the
classifier model identifies a correlation between (i) the provided
behavior data and symptom measurement data and (ii) data collected
in connection with individuals who have exhibited desirable
response times.
33. The computer-implemented method of claim 30, wherein the
correlation identifies at least one of an effective intervention,
treatment efficacy, and drug performance.
34. The computer-implemented method of claim 30, further comprising
applying the classifier model for classifying the individual with
respect to a likelihood of at least one of an onset or a
progression of the condition.
35. The computer-implemented method of claim 30, wherein the
classifier model identifies an impairment in the individual, said
impairment not currently being treated.
36. The computer-implemented method of claim 17, wherein symptom
data comprises performance metric data generated based on the
individual's interactions with a cognitive platform.
37. The computer-implemented method of claim 17, further comprising
a content module configured to generate one or more content queries
based at least in part on the behavior assessment data.
38. The computer-implemented method of claim 37, wherein the
content module is further configured to: submit the one or more
content queries to at least one content library comprising a
content index; and analyze content received from the at least one
content library to determine a relevance to a status of the
individual determined based on the behavior assessment data.
39. A computer-implemented method for managing the collection of
and access to behavior assessment data, the method comprising:
using one or more processors to execute instructions stored in one
or more memory storage devices comprising computer executable
instructions to perform operations including: receiving
instructions from a first user having authority to act on behalf of
an individual under study, the instructions including the
identification of a second user role and a third user role,
specification of one or more of behavior data, symptom measurement
data, and medicine regimen data associated with the individual
under study, and definition of access permissions for the second
and third user roles with respect to the behavior data and symptom
measurement data, wherein the symptoms and behaviors to be measured
are specified based on a condition of the individual under study;
receiving from users behavior data and symptom measurement data
observed from the individual under study according to either a
second user role or third user role based at least in part on the
access permissions specified by the first user; enforcing the
access permissions such that the second user role is limited to
providing and accessing a first subset of the behavior data and
symptom measurement data and the third user role is limited to
providing and accessing a second subset of the behavior data and
symptom measurement data; performing computational analysis on the
behavior data and symptom measurement data, thereby producing
behavior assessment data; and presenting the behavior assessment
data to the first user, second user, and third user in a manner
consistent with the defined access permissions specified by the
first user, wherein the condition is selected from the group
consisting of neuropsychological condition, a neurodegenerative
condition, or an executive function disorder.
40. The computer-implemented method of claim 39, wherein the
individual is the first user.
41. The computer-implemented method of claim 39, wherein the
condition is selected from the group consisting of dementia,
Parkinson's disease, cerebral amyloid angiopathy, familial amyloid
neuropathy, Huntington's disease, autism spectrum disorder (ASD),
presence of 16p11.2 duplication, attention deficit hyperactivity
disorder (ADHD), sensory-processing disorder (SPD), mild cognitive
impairment (MCI), Alzheimer's disease, multiple-sclerosis,
schizophrenia, major depressive disorder (MDD), or anxiety.
42. The computer-implemented method of claim 41, wherein the
individual is a child with attention deficit hyperactivity
disorder.
43. The computer-implemented method of claim 42, wherein the first
user is a parent of the child.
44. The computer-implemented method of claim 42, wherein the
behavior data comprises at least one of homework assignment
completion, frequency and quality of performing chores, and quality
of getting along with the parent acting on behalf of the child.
45. The computer-implemented method of claim 42, wherein the
symptom data comprises at least one of inattentiveness,
impulsivity, and hyperactivity.
46. The computer-implemented method of claim 42, wherein the
behavior assessment data comprises (i) pace of response of the
child to a treatment, (ii) a likelihood of onset and/or stage of
progression of ADHD, (iii) efficacy of medication at controlling a
behavior, and (iv) efficacy of medication at addressing a symptom
of ADHD.
47. The computer-implemented method of claim 42, further comprising
presenting contextually relevant content to the first, second, and
third users.
48. The computer-implemented method of claim 42, wherein the second
user is a teacher.
49. The computer-implemented method of claim 42, wherein the third
user is a healthcare provider and the behavior assessment data
supports the third user in a formulation of a course of treatment
or modification of a course of treatment.
50. The computer-implemented method of claim 41, wherein the
individual is an adult with a major depressive disorder.
51. The computer-implemented method of claim 50, wherein the first
user is the adult.
52. The computer-implemented method of claim 50, wherein the
behavior data comprises at least one of eating less, sleeping less,
experiencing unexplained aches and pains, reduced interaction with
friends and family, and absenteeism from work.
53. The computer-implemented method of claim 50, wherein the
symptom data comprises at least one of sadness, low self-esteem,
loss of motivation, irritability, and decreased energy.
54. The computer-implemented method of claim 50, wherein the
behavior assessment data comprises (i) pace of response of the
adult to a treatment, (ii) a likelihood of onset and/or stage of
progression of the major depressive disorder, (iii) efficacy of
medication at controlling a behavior, and (iv) efficacy of
medication at addressing a symptom of the major depressive
disorder.
55. The computer-implemented method of claim 50, further comprising
presenting contextually relevant content to the first, second, and
third users.
56. The computer-implemented method of claim 50, wherein the second
user is a family member of the individual.
57. The computer-implemented method of claim 50, wherein the third
user is a healthcare provider and the behavior assessment data
supports the third user in a formulation of a course of treatment
or modification of a course of treatment.
58. A system for managing the collection of and access to behavior
assessment data, the system comprising: one or more processors; and
a memory coupled with the one or more processors; wherein the one
or more processors execute a plurality of modules stored in the
memory, and wherein the plurality of modules comprises: a graphical
user interface at which: a first user having authority to act on
behalf of an individual under study identifies a second user role
and a third user role, specifies one or more of behavior data and
symptom measurement data associated with the individual under
study, and defines access permissions for the second and third user
roles with respect to the behavior data and symptom measurement
data, wherein the symptoms and behaviors to be measured are
specified based on a condition of the individual under study; and
users provide behavior data and symptom measurement data observed
from the individual under study according to either a second user
role or third user role based at least in part on the access
permissions specified by the first user; an authentication module
comprising computer-executable instructions for enforcing the
access permissions such that the second user role is limited to
providing and accessing a first subset of the behavior data and
symptom measurement data and the third user role is limited to
providing and accessing a second subset of the behavior data and
symptom measurement data; an analytics module comprising
computer-executable instructions for performing computational
analysis on the behavior data and symptom measurement data, thereby
producing behavior assessment data; and a reporting module for
presenting the behavior assessment data to the first user, second
user, and third user in a manner consistent with the defined access
permissions specified by the first user, wherein the condition is
selected from the group consisting of a neuropsychological
condition, a neurodegenerative condition, or an executive function
disorder.
59. The system of claim 58, wherein the individual is the first
user.
60. The system of claim 58, wherein the condition is selected from
the group consisting of dementia, Parkinson's disease, cerebral
amyloid angiopathy, familial amyloid neuropathy, Huntington's
disease, autism spectrum disorder (ASD), presence of 16p11.2
duplication, attention deficit hyperactivity disorder (ADHD),
sensory-processing disorder (SPD), mild cognitive impairment (MCI),
Alzheimer's disease, multiple-sclerosis, schizophrenia, major
depressive disorder (MDD), or anxiety.
61. The system of claim 60, wherein the individual is a child with
attention deficit hyperactivity disorder.
62. The system of claim 61, wherein the first user is a parent of
the child.
63. The system of claim 62, wherein the behavior data comprises at
least one of homework assignment completion, frequency and quality
of performing chores, and quality of getting along with the parent
acting on behalf of the child.
64. The system of claim 61, wherein the symptom data comprises at
least one of inattentiveness, impulsivity, and hyperactivity.
65. The system of claim 61, wherein the behavior assessment data
comprises (i) pace of response of the child to a treatment, (ii) a
likelihood of onset and/or stage of progression of ADHD, (iii)
efficacy of medication at controlling a behavior, and (iv) efficacy
of medication at addressing a symptom of ADHD.
66. The system of claim 61, wherein the reporting module presents
contextually relevant content to the first, second, and third
users.
67. The system of claim 61, wherein the second user is a
teacher.
68. The system of claim 61, wherein the third user is a healthcare
provider and the behavior assessment data supports the third user
in a formulation of a course of treatment or modification of a
course of treatment.
69. The system of claim 56, wherein the individual is an adult with
a major depressive disorder.
70. The system claim 69, wherein the first user is the adult.
71. The system of claim 69, wherein the behavior data comprises at
least one of eating less, sleeping less, experiencing unexplained
aches and pains, reduced interaction with friends and family, and
absenteeism from work.
72. The system of claim 69, wherein the symptom data comprises at
least one of sadness, low self-esteem, loss of motivation,
irritability, and decreased energy.
73. The system of claim 69, wherein the behavior assessment data
comprises (i) pace of response of the adult to a treatment, (ii) a
likelihood of onset and/or stage of progression of the major
depressive disorder, (iii) efficacy of medication at controlling a
behavior, and (iv) efficacy of medication at addressing a symptom
of the major depressive disorder.
74. The system of claim 69, wherein the reporting module presents
contextually relevant content to the first, second, and third
users.
75. The system of claim 69, wherein the second user is a family
member of the individual.
76. The system of claim 69, wherein the third user is a healthcare
provider and the behavior assessment data supports the third user
in a formulation of a course of treatment or modification of a
course of treatment.
Description
CROSS REFERENCE TO RELATED APPLICATION
[0001] This application claims priority to and the benefit of U.S.
Provisional Patent Application Ser. No. 62/482,648 filed on Apr. 6,
2017, the disclosure of which is hereby incorporated by reference
in its entirety.
FIELD OF THE DISCLOSURE
[0002] The present disclosure relates generally to a solutions
platform configured for the secured collection and analysis of
data, and the secured sharing of content between platforms. More
particularly, embodiments relate to a solutions platform configured
to facilitate monitoring and/or improved treatment of
neuropsychological conditions.
BACKGROUND
[0003] Monitoring and/or improved treatment of a variety of
different conditions is desirable, including in connection with
dementia, Parkinson's disease, cerebral amyloid angiopathy,
familial amyloid neuropathy, Huntington's disease, or other
neurodegenerative condition, autism spectrum disorder (ASD),
presence of the 16p11.2 duplication, and/or executive function
disorders, including attention deficit hyperactivity disorder
(ADHD), sensory-processing disorder (SPD), mild cognitive
impairment (MCI), Alzheimer's disease, multiple-sclerosis,
schizophrenia, major depressive disorder (MDD), or anxiety. For
example, patients, caregivers (including parents), and medical
practitioners have indicated, including in user research and from
clinical trial results, that heath tips related to ADHD symptoms
and impairments would be helpful so they can take appropriate
action (including upon consultation with a medical practitioner or
healthcare provide where appropriate). Current drawbacks with the
availability of content are lack of personalization and relevance
to a patient's daily and weekly challenges and their symptoms.
SUMMARY
[0004] One of the biggest challenges for individuals with such
conditions, or parents, custodians, and/or caregivers (including
teachers) of individuals with such conditions, or healthcare
providers, is the lack or inadequacy of systems that track and
monitor over time the behavior and symptoms of the individual with
a given condition. For example, where the individual has ADHD
(whether an adult or a child), the capability to monitor securely
the individual's symptoms and behaviors both at home and at school
can be critical in the management and treatment of the condition.
As another example, for an individual with depression (whether an
adult or a child), the capability to monitor securely the
individual's symptoms and behaviors can be beneficial for managing
and treating the condition. Such data can assist the healthcare
provider for an individual to determine what type of treatment
and/or medication is effective or ineffective, to determine whether
adjustments are to be made to the individual's treatment plan.
[0005] In view of the foregoing, apparatus, systems and methods are
provided for monitoring and/or tracking at least one symptom and
related behavior of a condition in an individual. In some examples,
the apparatus, systems and methods are configured to analyze data
indicative of the cognitive abilities of the individual having the
condition, to provide insight into the relative health or strength
of about portions of the brain of the individual. In certain
configurations, the example apparatus, systems and methods can be
configured to analyze data indicative of the status or progress of
treatments for enhancing certain cognitive abilities of the
individual having the condition.
[0006] Some advantages provided by embodiments of the disclosure
include: [0007] Providing patients and/or their caregivers
(including parents) with simple tools to organize and track the
health information of the individual with the condition, without
recommendations to alter or change a previously prescribed
treatment or therapy and share this information with the
individual's health care provider as part of a management plan for
a condition of an individual (including a disease-management plan);
[0008] Helping patients and/or their caregivers (including parents)
self-manage their condition, including where the condition is a
disease (including a child's disease or condition) without
providing specific treatment or treatment suggestions; [0009]
Providing easier access to information related to patients' health
conditions or treatments; [0010] Supplementing professional
clinical care by facilitating behavioral change, or coaching
patients and/or their caregivers (including parents) with simple
prompting and with methods applicable in clinical practice
(including some routine methods); [0011] Providing physician
contextually relevant information by matching patient-specific
information (e.g. diagnosis, treatments, signs or symptoms) to
reference information routinely used in clinical practice to
facilitate a physician's assessment of a specific patient; and
[0012] Providing patients and providers with mobile access to
health record systems or enable to gain electronic access to health
information stored.
[0013] Accordingly, embodiments of the disclosure allow patients
and/or their caregivers (including parents) to see helpful
information based on the child's/patient's data and the community's
data, so that the patients and/or their caregivers (including
parents) can feel empowered and to help them to take the next
appropriate action, including in consultation with the
child's/patient's doctor (where appropriate).
[0014] In an aspect, embodiments of the disclosure relate to a
system for managing the collection of and access to behavior
assessment data. The system includes one or more processors; and a
memory coupled with the one or more processors. The one or more
processors execute a plurality of modules stored in the memory. The
modules include a graphical user interface at which a first user
having authority to act on behalf of an individual under study (i)
identifies a second user role and a third user role, (ii) specifies
one or more of behavior data, symptom measurement data, and
medicine regimen data associated with the individual under study,
and (iii) defines access permissions for the second and third user
roles with respect to the behavior data and symptom measurement
data. The symptoms and behaviors to be measured are specified based
on a condition of the individual under study. Users provide
behavior data and symptom measurement data observed from the
individual under study according to either a second user role or
third user role based at least in part on the access permissions
specified by the first user.
[0015] An authentication module includes computer-executable
instructions for enforcing the access permissions such that the
second user role is limited to providing and accessing a first
subset of the behavior data and symptom measurement data and the
third user role is limited to providing and accessing a second
subset of the behavior data and symptom measurement data.
[0016] An analytics module includes computer-executable
instructions for performing computational analysis on the behavior
data and symptom measurement data, thereby producing behavior
assessment data.
[0017] A reporting module presents the behavior assessment data to
the first user, second user, and third user in a manner consistent
with the defined access permissions specified by the first
user.
[0018] One or more of the following features may be included. The
individual may be the first user. The analytics module may apply a
classifier model to the provided behavior data and symptom
measurement data to create a composite profile. The classifier
model may include linear/logistic regression, principal component
analysis, generalized linear mixed models, random decision forests,
support vector machines, and/or artificial neural networks. The
classifier model may identify a correlation between (i) the
provided behavior data and symptom measurement data and (ii) data
collected in connection with individuals who have exhibited
desirable treatment response times. The correlation may identify at
least one of an effective intervention, treatment efficacy, and
drug performance. The classifier model may identify an impairment
in the individual, the impairment not currently being treated. The
analytics module may apply a classifier model for classifying the
individual with respect to a likelihood of at least one of an onset
or a progression of the condition.
[0019] The behavior assessment data supports a formulation of a
course of treatment or modification of a course of treatment.
[0020] A usage analytics database may for store and provide usage
analytics data to the analytics module.
[0021] An accounts and profiles database may store and transmit
user account and profile data to the authentication module.
[0022] A preference database may store and transmit user
preferences to the authentication module.
[0023] A health database may store and transmit health information
data to the reporting module.
[0024] The symptom data may include performance metric data
generated based on the individual's interactions with a cognitive
platform.
[0025] A content module may be configured to generate one or more
content queries based at least in part on the behavior assessment
data. The content module may be further configured to submit the
one or more content queries to at least one content library
including a content index, and to analyze content received from the
at least one content library to determine a relevance to a status
of the individual determined based on the behavior assessment
data.
[0026] In another aspect, embodiments of the disclosure relate to a
computer-implemented method for managing the collection of and
access to behavior assessment data. The method includes using one
or more processors to execute instructions stored in one or more
memory storage devices including computer executable instructions
to perform operations including receiving instructions from a first
user having authority to act on behalf of an individual under
study. The instructions include the identification of a second user
role and a third user role, specification of one or more of
behavior data and symptom measurement data associated with the
individual under study, and definition of access permissions for
the second and third user roles with respect to the behavior data
and symptom measurement data. The symptoms and behaviors to be
measured are specified based on a condition of the individual under
study.
[0027] Behavior data and symptom measurement data observed from the
individual under study is received from according to either a
second user role or third user role based at least in part on the
access permissions specified by the first user. Access permissions
are enforced such that the second user role is limited to providing
and accessing a first subset of the behavior data and symptom
measurement data and the third user role is limited to providing
and accessing a second subset of the behavior data and symptom
measurement data.
[0028] Computational analysis is performed on the behavior data and
symptom measurement data, thereby producing behavior assessment
data. The behavior assessment data is presented to the first user,
second user, and third user in a manner consistent with the defined
access permissions specified by the first user.
[0029] One or more of the following features may be included. The
individual may be the first user. The individual may be a child.
The individual may be an adult.
[0030] At least a portion of the behavior data may include
measurements of a behavior based on diagnostic and symptom criteria
for a given condition. The behavior data may include at least one
of homework assignment completion, frequency and quality of
performing chores, and quality of getting along with a person
acting on behalf of the individual.
[0031] At least a portion of the symptom measurement data may
include measurements of a symptom on a clinically validated symptom
list for a given condition. The symptom measurement data may
include physiological data, such as, e.g., electrical activity,
heart rate, blood flow, and/or oxygenation levels.
[0032] The first user may be a parent of the individual. The second
user may be a teacher of the individual. The third user may be a
practitioner treating the individual.
[0033] The behavior assessment data may include (i) pace of
response of the individual to a treatment, (ii) a likelihood of
onset and/or stage of progression of the condition, (iii) efficacy
of medication at controlling a behavior, and/or (iv) efficacy of
medication at addressing a symptom of the condition.
[0034] Performing computational analysis may include applying a
classifier model to the behavior data and symptom measurement data
to create a composite profile. The classifier model may include
linear/logistic regression, principal component analysis,
generalized linear mixed models, random decision forests, support
vector machines, and/or artificial neural networks.
[0035] The classifier model may identify a correlation between (i)
the provided behavior data and symptom measurement data and (ii)
data collected in connection with individuals who have exhibited
desirable response times. The correlation may identify an effective
intervention, treatment efficacy, and/or drug performance.
[0036] A classifier model may be applied for classifying the
individual with respect to a likelihood of at least one of an onset
or a progression of the condition. The classifier model may
identify an impairment in the individual that is not currently
being treated.
[0037] Symptom data may include performance metric data generated
based on the individual's interactions with a cognitive
platform.
[0038] A content module may be configured to generate one or more
content queries based at least in part on the behavior assessment
data. The content module may be configured to submit the one or
more content queries to at least one content library including a
content index. Content received from the at least one content
library may be analyzed to determine a relevance to a status of the
individual determined based on the behavior assessment data.
[0039] In yet another aspect, a computer-implemented method for
managing the collection of and access to behavior assessment data
includes using one or more processors to execute instructions
stored in one or more memory storage devices including computer
executable instructions to perform operations including receiving
instructions from a first user having authority to act on behalf of
an individual under study, the instructions including the
identification of a second user role and a third user role,
specification of one or more of behavior data, symptom measurement
data, and medicine regimen data associated with the individual
under study, and definition of access permissions for the second
and third user roles with respect to the behavior data and symptom
measurement data, wherein the symptoms and behaviors to be measured
are specified based on a condition of the individual under
study.
[0040] Behavior data and symptom measurement data from users is
received, the data being observed from the individual under study
according to either a second user role or third user role based at
least in part on the access permissions specified by the first
user.
[0041] The access permissions are enforced such that the second
user role is limited to providing and accessing a first subset of
the behavior data and symptom measurement data and the third user
role is limited to providing and accessing a second subset of the
behavior data and symptom measurement data.
[0042] Computational analysis on the behavior data and symptom
measurement data is performed, thereby producing behavior
assessment data. The behavior assessment data is presented to the
first user, second user, and third user in a manner consistent with
the defined access permissions specified by the first user.
[0043] The condition is a neuropsychological condition, a
neurodegenerative condition, or an executive function disorder.
[0044] One or more of the following features may be included. The
individual may be the first user. The condition may be dementia,
Parkinson's disease, cerebral amyloid angiopathy, familial amyloid
neuropathy, Huntington's disease, autism spectrum disorder (ASD),
presence of 16p11.2 duplication, attention deficit hyperactivity
disorder (ADHD), sensory-processing disorder (SPD), mild cognitive
impairment (MCI), Alzheimer's disease, multiple-sclerosis,
schizophrenia, major depressive disorder (MDD), or anxiety.
[0045] The individual may be a child with attention deficit
hyperactivity disorder.
[0046] The first user may be a parent of the child. The behavior
data may include homework assignment completion, frequency and
quality of performing chores, and/or quality of getting along with
the parent acting on behalf of the child. The symptom data may be
inattentiveness, impulsivity, and/or hyperactivity.
[0047] The behavior assessment data may include (i) pace of
response of the child to a treatment, (ii) a likelihood of onset
and/or stage of progression of ADHD, (iii) efficacy of medication
at controlling a behavior, and/or (iv) efficacy of medication at
addressing a symptom of ADHD.
[0048] Contextually relevant content may be presented to the first,
second, and third users. The second user may be a teacher. The
third user may be a healthcare provider, and the behavior
assessment data may support the third user in a formulation of a
course of treatment or modification of a course of treatment.
[0049] The individual may be an adult with a major depressive
disorder. The first user may be the adult.
[0050] The behavior data may be eating less, sleeping less,
experiencing unexplained aches and pains, reduced interaction with
friends and family, and/or absenteeism from work. The symptom data
may include sadness, low self-esteem, loss of motivation,
irritability, and decreased energy. The behavior assessment data
may include (i) pace of response of the adult to a treatment, (ii)
a likelihood of onset and/or stage of progression of the major
depressive disorder, (iii) efficacy of medication at controlling a
behavior, and (iv) efficacy of medication at addressing a symptom
of the major depressive disorder.
[0051] Contextually relevant content may be presented to the first,
second, and third users. The second user may be a family member of
the individual. The third user may be a healthcare provider, and
the behavior assessment data may support the third user in a
formulation of a course of treatment or modification of a course of
treatment.
[0052] In still another aspect, embodiments of the disclosure
relate to a system for managing the collection of and access to
behavior assessment data, the system including one or more
processors and a memory coupled with the one or more processors.
The one or more processors execute a plurality of modules stored in
the memory, the plurality of modules including a graphical user
interface at which a first user having authority to act on behalf
of an individual under study identifies a second user role and a
third user role, specifies one or more of behavior data and symptom
measurement data associated with the individual under study, and
defines access permissions for the second and third user roles with
respect to the behavior data and symptom measurement data, wherein
the symptoms and behaviors to be measured are specified based on a
condition of the individual under study. Users provide behavior
data and symptom measurement data observed from the individual
under study according to either a second user role or third user
role based at least in part on the access permissions specified by
the first user.
[0053] An authentication module includes computer-executable
instructions for enforcing the access permissions such that the
second user role is limited to providing and accessing a first
subset of the behavior data and symptom measurement data and the
third user role is limited to providing and accessing a second
subset of the behavior data and symptom measurement data.
[0054] An analytics module includes computer-executable
instructions for performing computational analysis on the behavior
data and symptom measurement data, thereby producing behavior
assessment data. A reporting module presents the behavior
assessment data to the first user, second user, and third user in a
manner consistent with the defined access permissions specified by
the first user.
[0055] The condition a neuropsychological condition, a
neurodegenerative condition, or an executive function disorder.
[0056] One or more of the following features may be included. The
individual may be the first user. The condition may be dementia,
Parkinson's disease, cerebral amyloid angiopathy, familial amyloid
neuropathy, Huntington's disease, autism spectrum disorder (ASD),
presence of 16p11.2 duplication, attention deficit hyperactivity
disorder (ADHD), sensory-processing disorder (SPD), mild cognitive
impairment (MCI), Alzheimer's disease, multiple-sclerosis,
schizophrenia, major depressive disorder (MDD), or anxiety.
[0057] The individual may be a child with attention deficit
hyperactivity disorder. The first user may be a parent of the
child.
[0058] The behavior data may include homework assignment
completion, frequency and quality of performing chores, and/or
quality of getting along with the parent acting on behalf of the
child. The symptom data may be inattentiveness, impulsivity, and/or
hyperactivity. The behavior assessment data may include (i) pace of
response of the child to a treatment, (ii) a likelihood of onset
and/or stage of progression of ADHD, (iii) efficacy of medication
at controlling a behavior, and/or (iv) efficacy of medication at
addressing a symptom of ADHD.
[0059] The reporting module may present contextually relevant
content to the first, second, and third users. The second user may
be a teacher. The third user may be a healthcare provider and the
behavior assessment data may support the third user in a
formulation of a course of treatment or modification of a course of
treatment.
[0060] The individual is an adult with a major depressive disorder.
The first user may be the adult.
[0061] The behavior data may include eating less, sleeping less,
experiencing unexplained aches and pains, reduced interaction with
friends and family, and/or absenteeism from work. The symptom data
may be at least one of sadness, low self-esteem, loss of
motivation, irritability, and/or decreased energy. The behavior
assessment data may include (i) pace of response of the adult to a
treatment, (ii) a likelihood of onset and/or stage of progression
of the major depressive disorder, (iii) efficacy of medication at
controlling a behavior, and/or (iv) efficacy of medication at
addressing a symptom of the major depressive disorder.
[0062] The reporting module may presents contextually relevant
content to the first, second, and third users. The second user may
be a family member of the individual. The third user may be a
healthcare provider, and the behavior assessment data may support
the third user in a formulation of a course of treatment or
modification of a course of treatment.
[0063] In this aspect or any one or more of the other aspects
described herein, the individual can be the first user.
[0064] The exemplary system, method or apparatus (including an App)
described herein provides targeted output based on data gathered
from the objective and observational measures of the patients
symptoms and certain behaviors exhibited by the patient. The type
of the conditions (e.g., cognitive deficit) determines the symptoms
measured and the behaviors that are tracked. The behavior data is
based on validated behavior scales (such as but not limited to the
Vanderbilt scale). The specific symptoms and behaviors are relevant
to individual based on his/her condition (cognitive deficit). The
exemplary system, method or apparatus (including an App) allows a
unique level of personalization, including in an automated way,
resulting in predictive contents that are output from the exemplary
system, method or apparatus (including an App). The output can be
presented or transmitted in any way specified by the first user
(i.e., the user that sets permissions for the other users, also
referred to herein as user 1).
[0065] The first user specifies the care-team for the individual
under study, and specifies/delegates the symptoms and behaviors of
the individual for which each of the other user is to provide data.
For example, if the condition (cognitive deficit) is ADHD and the
individual under study is a child, the first user can be a parent,
guardian, or other caregiver. The first user grants levels of
permissions to a teacher, medical practitioner, etc. The parent
provides data indicative of information such as type of medication
the child is on, amount of dosage/dose titration, consistency of
dose regimen; data indicative of symptoms such as attentiveness,
impulsiveness, level of activity; and behaviors such as success or
failure at completion of homework assignment, "acting up", ability
to sit still, taking direction, etc. A teacher can provide data
relevant to each or some of the symptoms and behaviors at differing
times of the day and in differing situations/contexts. The
exemplary system, method or apparatus (including an App)
facilitates collection of measurement data throughout day, to more
accurately assess behaviors and level of symptoms in the individual
in different contexts (e.g., school, homes, medical office,
etc.).
[0066] The first user has the ability to personalize behaviors and
symptoms specifically to the patient/individual under study. For
example, since every child is different, the exemplary system,
method or apparatus (including an App) facilitates tracking over
time to assess the progress of the child, and the first user (e.g.,
the patient, a caregiver or a parent) can get measurement-based
care for the child, and can tailor the care of the individual to
address elements of the child's impairment that are not responding
to treatment, behavior therapy, and/or medication.
[0067] The ability for the system and processes to track and
monitor personalized symptoms and behaviors is specific from
patient to patient. By understanding what symptoms are progressing
or not progressing over time, the results/output of the analysis
from the exemplary system, method or apparatus (including an App)
over time can be used to indicate symptoms that are not yet being
treated. As a result, the exemplary system, method or apparatus
(including an App) can act like a biomarker for symptoms that are
not adequately responding to treatment. These particular
combinations/data trends of symptom and behaviors can become
representative of a particular impairment(s) that can afflict a
child.
[0068] The symptoms assessed using the symptom data are determined
based on the condition (cognitive deficit) of the individual (e.g.,
a neurodegenerative condition or an executive function disorder).
The symptoms assessed are based on validated instruments for
symptoms of a condition (e.g., based on validated instruments used
by medical professionals). The behaviors to be measured are
developed based on these symptoms, e.g., when a symptom is
inattentiveness, the behaviors monitored can be degree of
completion of homework, etc. The symptoms and behaviors tracked
over time can be customized based on the individual child's
treatment needs, such as behaviors set based on the functional
ability of child to act in a healthy way.
[0069] The measurement-based care can be tuned/tweaked to each
individual patient over time. This allows informed and better
treatment decisions for identifying and treating the impairments
not being initially treated in the patient. Accordingly, the
exemplary system, method or apparatus (including an App) enables
the identification of untreated impairments.
[0070] The exemplary system, method or apparatus (including an App)
and process also tracks the current treatments the patient is
taking, and also tracks side effects and/or adverse events and
prepares a narrative on how a treatment (e.g., a drug for ADHD or
depression) affects the patient. For example, a drug treatment for
ADHD can result in appetite suppression, weight gain, anxiety,
and/or disrupted sleep pattern, because of an improper/insufficient
dosage, or drug interactions. The data and analysis from the
exemplary system, method or apparatus (including an App) all adds
to information for measurement-based care of the individual for
better treatment outcome for the patient in a way interpretable by
stakeholder/patient.
[0071] Based on the results of the data analysis, output can be
displayed/transmitted to the first user based on the analysis
results indicative of specific impairment(s) of the individual. The
process and exemplary system, method or apparatus (including an
App) is configured to algorithmically identify through another
course and resource specific support
programs/initiatives/programs/other items that the first user
(e.g., parent) can implement to address the impairment(s). This can
include practical health advice. The exemplary system, method or
apparatus (including an App) can be configured to formulate queries
to send out to external resources based on the impairment(s)
identified from the analysis. The queries to get the health
advice/initiatives/programs/other information can be sent out to a
contents library specific to an indication (e.g., ADHD, depression,
etc.), or to a society that serves a particular community (e.g.,
CHADD for ADHD), a national resource center, or journal or other
literature specific to the patient's satiation (e.g., the child).
The exemplary system, method or apparatus (including an App) can
help the first user download the articles/resources/behavioral
therapy resource or provide other ways to get access to the
targeted resource. For example, when the symptom/behavior data
indicates that the child patient has difficulty completing
homework, the exemplary system, method or apparatus (including an
App) can identify resources to help the child do homework (e.g.,
study tips or exercises).
[0072] The exemplary system, method or apparatus (including an App)
maps out predictive content that can be used to help identify
appropriate resources to the first user regarding what could help
the patient (e.g., child or adult). The data and analysis from each
patient of a plurality of patients can be used to build a database
based on hundreds of patients, and their responses or lack of
responses to a treatment, the impairment(s) identified, and their
responses to the resources presented. The provides predictive
models based on the previous experience of hundreds of patients.
Accordingly, the first user can know before trying whether a
particular new drug or other treatment, or what dosage of the drug
or other treatment, are most likely to help patient (e.g., child or
adult) or what may potentially exacerbate an impairment or a poorly
treated symptom or a poorly managed behavior. The exemplary system,
method or apparatus (including an App) may also indicate the
possible outcome that may be attained if the individual follows the
recommendation.
BRIEF DESCRIPTION OF FIGURES
[0073] FIG. 1 is a block diagram of an exemplary apparatus for
implementing certain functionalities of the solutions platform
including the analytics engine (including classifier model) and
report generator, in accordance with an embodiment of the
disclosure;
[0074] FIG. 2 is a block diagram of an exemplary network
environment suitable for a distributed implementation of the
solutions platform, in accordance with an embodiment of the
disclosure;
[0075] FIG. 3 is a block diagram of another exemplary network
environment suitable for a distributed implementation of the
solutions platform, in accordance with an embodiment of the
disclosure;
[0076] FIG. 4 is a block diagram of yet another exemplary network
environment suitable for a distributed implementation of the
solutions platform, in accordance with an embodiment of the
disclosure;
[0077] FIG. 5A and FIG. 5B are block diagram of other exemplary
network environments suitable for distributed implementations of
the solutions platform, in accordance with embodiments of the
disclosure;
[0078] FIG. 6 is a block diagram of an exemplary computing device
that can be used as a computing component to perform one or more of
the procedures described herein, including in connection with FIGS.
1-4;
[0079] FIG. 7 is a flowchart of an exemplary method that can be
implemented using any solutions platform described herein that
executes processor-executable instructions using at least one
server, in accordance with embodiments of the disclosure;
[0080] FIGS. 8A-8B are flowcharts of another method that can be
implemented using a solutions platform that includes at least one
processing unit and at least one server, in accordance with
embodiments of the disclosure;
[0081] FIG. 9 is a flow diagram showing an example of the types of
permissions that can be set on the solutions platform based on the
control signals set by a user 1, in accordance with embodiments of
the disclosure;
[0082] FIG. 10 is a flowchart of an exemplary use of a solutions
platform by user 1, including setting permission levels and access
types, and indicating the type of data and other information that
user 1 is given the capability to enter at a rendered graphical
user interface, in accordance with an embodiment of the
disclosure;
[0083] FIGS. 11A-11D are tables of examples of the types of data
and other information that can be included in an enhanced analysis
report, in accordance with an embodiment of the disclosure;
[0084] FIGS. 12A-12B are graphical representations of the graphical
user interfaces that the solutions platform can be configured to be
rendered to allow user 1, user type 2, and/or user type 3, as
applicable, to enter quantifiers of behavior measures or symptom
measures, the types of measurement fields that can be rendered for
display at the graphical user interface for entry of the ratings
and scales by user 1, user type 2, and/or user type 3, and the
types of rating and quantification scales that can be provided in
the measurement fields, in accordance with embodiments of the
disclosure;
[0085] FIG. 13 is a rendered view of a landscape that may be used
in a spatial navigation task, in accordance with an embodiment of
the disclosure; and
[0086] FIGS. 14A-14D and 15A-15H are graphical representations of
exemplary user interfaces that can be rendered using exemplary
systems, methods, and apparatus to render the tasks and/or
interferences (either or both with computerized element) for user
interactions, and which may also be used for one or more of: to
collect data indicative of the individual's responses to the tasks
and/or the interferences and the computerized element, to show
progress metrics, or to provide the analysis metrics.
DETAILED DESCRIPTION
[0087] It should be appreciated that all combinations of the
concepts discussed in greater detail below (provided such concepts
are not mutually inconsistent) are contemplated as being part of
the inventive subject matter disclosed herein. It also should be
appreciated that terminology explicitly employed herein that also
may appear in any disclosure incorporated by reference should be
accorded a meaning most consistent with the particular concepts
disclosed herein.
[0088] As used herein, the term "includes" means includes but is
not limited to, the term "including" means including but not
limited to. The term "based on" means based at least in part
on.
[0089] Following below are more detailed descriptions of various
concepts related to, and embodiments of, inventive methods,
apparatus and systems comprising a solutions platform configured
for the secured collection and analysis of data, and the secured
sharing of content between platforms. The content can be, but is
not limited to, the collected data and/or the results of the data
analysis.
[0090] The solutions platform can be coupled with one or more types
of measurement components, for receiving and analyzing data
collected from at least one measurement of the one or more
measurement components. As non-limiting examples, the measurement
component can be a physiological component.
[0091] The solutions platform can be coupled with one or more types
of cognitive platforms, for analyzing data collected from user
interaction with the cognitive platform. As non-limiting examples,
the cognitive platform and/or platform product can be configured
for cognitive monitoring, cognitive assessment, cognitive
screening, and/or cognitive treatment, including for clinical
purposes. The data from the cognitive platform can be used by the
exemplary systems, methods, and apparatus disclosed herein as
symptom measurement data.
[0092] As a non-limiting example, the cognitive platform can be
based on the Project: EVO.TM. platform by Akili Interactive Labs,
Inc. (Boston, Mass.).
[0093] The exemplary solutions platform can be implemented to
facilitate monitoring and/or improved treatment of a variety of
different conditions, such as but not limited to neuropsychological
conditions, including dementia, Parkinson's disease, cerebral
amyloid angiopathy, familial amyloid neuropathy, Huntington's
disease, or other neurodegenerative condition, autism spectrum
disorder (ASD), presence of the 16p11.2 duplication, and/or
executive function disorders, including attention deficit
hyperactivity disorder (ADHD), sensory-processing disorder (SPD),
mild cognitive impairment (MCI), Alzheimer's disease,
multiple-sclerosis, schizophrenia, major depressive disorder (MDD),
or anxiety.
[0094] In a non-limiting example, the solutions platform can be
configured to facilitate monitoring and/or improved treatment of
ADHD. Symptoms of ADHD include inattentiveness, impulsivity and
hyperactivity. Both children and adults can have ADHD, however, the
symptoms can be exhibited beginning in childhood. ADHD can be
considered a chronic disease in certain aspects. Once diagnosed, it
is typically treated with medications and managed through
behavioral therapies.
[0095] One of the biggest challenges for individuals with ADHD, or
parents, custodians, and/or caregivers (including teachers) of
individuals with ADHD, or healthcare providers, is the lack or
inadequacy of systems that track and monitor over time the behavior
and symptoms of the individual with ADHD. For example, where the
individual with ADHD is a child, the capability to monitor securely
the individual's behaviors both at home and at school can be
critical in the management and treatment of the condition
(including a disease). Such data can assist the healthcare provider
for an individual in determining what type of treatment and/or
medication is effective or ineffective, in order to determine
whether adjustments are to be made to the individual's treatment
plan.
[0096] In any example herein, the term "healthcare provider"
encompasses one or more of a physician (including a pediatrician
and/or a behavioral specialist), a nurse, a physician's assistant,
a psychologist, a psychiatrist, and the supporting clinical and
administrative office staff of a healthcare or medical
facility.
[0097] In a non-limiting implementation, the solutions platform can
be configured to include components that facilitate the collection
of data indicative of behavior metrics and symptom metrics for
activities of an individual (such as but not limited to a child
with ADHD), components that facilitate the capture of data
indicative of a status or progress of a treatment plan for a
condition of the individual, and components that provide meaningful
analysis of the data.
[0098] In an example, the solutions platform can be configured to
collect and analyze content that assist with monitoring progress
and/or modifying the individual's treatment plan
[0099] In another example, the solutions platform can be configured
to assist in improving the results of a treatment using
visualizations.
[0100] In another example, the solutions platform can be configured
as an application (App) for use by a parent, custodian, guardian or
other caregiver of a child. The solutions platform in this example
can be configured to provide secured, authenticated access for the
collection of data indicative of behavior measures and/or symptom
measures. The solutions platform gives parent, custodian, guardian
or other caregiver of the child the capability to control the level
and type of access that another user can have to the platform,
thereby facilitating the collection of data indicative of behavior
measures and/or symptom measures from other user through a secured
access (such as but not limited to a secured login).
[0101] In this non-limiting exemplary implementation, the solutions
platform may be configured such that an individual (including a
parent, custodian, or other caregiver of an individual) may
download an aspect of it as an App and use the App to collect
behavior and symptom data about the individual (including the
child) on a regular basis. The App provides reminders and
encouragement to ensure consistent, long-term engagement by the
individual (including a parent, custodian, or other caregiver of an
individual). The App is configured to provide the primary user the
capability to request behavior data from another designated user
(such as but not limited to a teacher or other caregiver of the
individual). The request may be sent through a secured invitation
delivered via email or other means. As an example, when the other,
secondary user receives the invitation, they access the secure link
provided, provide the information requested to set up an account
(such as but not limited to login credentials) to be accessed at
intervals to enter data and other information in the measurement
fields provided (such as but not limited to information on how the
child is behaving in school or to quantify measures of the
individual's symptoms).
[0102] Such an App may also provide the capability for an
individual (including a parent, custodian, or other caregiver of an
individual) to enter information from an assessment (such as but
not limited to the Vanderbilt assessment scales) and derive
quantifiable measures that track treatment data such as but not
limited to compliance to taking medication, attendance to physician
appointments, response to behavioral therapy, etc.
[0103] In any example herein, the solutions platform provides user
1 with the capability and graphical user interfaces to set the
types of alerts and notifications to be sent to user types 2 and
user types 3 For example, the graphical user interfaces can provide
user 1 with the capability to set alerts and notifications
according to the permission and access levels set, such as but not
limited to, when enhanced analysis reports (including progress
reports) are available for viewing by those with the appropriate
permission levels, when progress is improving or declining in order
to monitor the progress of the individual's treatment (such as but
not limited to a child's treatment, when the individual meets a
given treatment milestone (including whether such milestone is
arrived at by using a certain dosage or regimen of a drug,
pharmaceutical agent, biologic, or other medication, when an
individual's treatment performance is improving, stable, or
declining (including by computing a projected performance level for
the individual), when user 1 or other user type is required to
retrieve a graphical user interface to complete a symptom tracker,
a behavior checklist, or other metric, in the solutions
platform.
[0104] In non-limiting examples of a solutions platform for use in
ADHD, the type of behavior metrics that are quantified can be
behavior metrics related frequency and quality of homework
assignment completion, frequency and quality of performing chores,
and the quality of getting along with the parents, custodians,
guardians, or other individual acting on behalf of a child. The
exemplary symptom metrics can be set using scores from other
symptom trackers.
[0105] In non-limiting examples of a solutions platform for use in
ADHD, an enhanced analysis report can be generated to provide data,
analysis and visualizations indicative of any presence of ADHD
symptoms based on data entered by user 1, or other user types, the
presence of ADHD symptoms based on scores of an assessment tool
used at diagnosis, the presence of ADHD-related problems and
symptoms specific to the patient (for example, a parent's
assessment of unruly behavior in school), a measure of an
individual patient's improvement and effort within the treatment, a
measure created by the solutions platform (including using a
classifier) to compute a projection that indicates a higher or
lower likelihood of treatment improvement or decline, an indication
of an individual's or parent (or other user 1 type) satisfaction or
dissatisfaction with the progress of a treatment, a metric of
progress of a treatment, user 1 or other user type (e.g., parent
and teacher) assessment or report, number of daily treatments
completed per week or month was desired to be quantified.
[0106] Based on permissions (using control signals) set by the
primary user, the solutions platform can be configured to analyze
the data collected from the individual (including a parent,
custodian, or other caregiver of an individual). The solutions
platform can be configured to generate enhanced analysis reports
based on the analysis and provide the report to the individual
(including a parent, custodian, or other caregiver of the
individual) in visualizations that provide them with information on
how the individual is progressing over time. Based on permissions
(using control signals) set by the primary user, these
visualizations can be shared with the individual's healthcare
provider (such as but not limited to a child's physician) and used
to discuss progress and/or modification to a treatment plan for the
individual. Based on permissions (using control signals) set by the
primary user, the solutions platform can be configured to allow the
healthcare provider to view the enhanced analysis report (including
any data). For example, the healthcare provider may view the
enhanced analysis report from the parents App and/or the parent can
email the healthcare provider an invitation to set up an account to
log into the solutions platform (once authenticated) and view the
data collected on the child.
[0107] The solutions platform is configured to allow a primary user
to work with a healthcare provider to determine the behaviors to be
presented in the measurement fields of the solutions platform and
quantified using the solution platform.
[0108] The exemplary solutions platform allows users (such as but
not limited to parent, teacher, physicians, behavioral therapists,
etc), to provide quantifiable measures of a variety of symptoms,
also captures data from actual treatments (such as but not limited
to scores from a cognitive treatment and other treatment), analyzes
the collected data, an generates an enhanced analysis report that
presents the data and analysis results in a form of interpretable,
meaningful metrics, which can be used to determine if treatment is
progressing adequately or satisfactorily.
[0109] The enhanced analysis report can be used in consultation
with a healthcare provider to evaluate the individual's response to
the treatment, determine any modifications to be made to the
treatment, the overall time period for implementation of the
modifications to the treatment, etc., in order to derive a stable
outcome or an improved outcome of the treatment for the individual.
This can result in a better condition management (including disease
management) outcome for the individual.
[0110] In any example herein, the solutions platform provides
control settings such that the access level and permissions for a
secondary user set by a primary user may be revoked or
overruled.
[0111] The exemplary systems, methods, and apparatus according to
the principles herein provide a set of management solutions and
services that are configured to collect data indicative of
behaviors and symptoms of a subject having a condition, in order to
quantifiably track and monitor the subject's progress with one or
more treatments (such as but not limited to ADHD treatments).
[0112] While the capabilities and functionalities of the solutions
platform can be described relative to an implementation directed to
ADHD, the solutions platform of the instant disclosure is not so
limited. An exemplary solutions platform can be directed to other
types of conditions, including neuropsychological conditions and/or
other executive function disorders.
[0113] Exemplary systems, methods, and apparatus herein provide a
solutions platform that is configured to provide controlled access
via a distributed network to distributed data assets, and to
generate enhanced analysis reports based on the data assets.
[0114] It should be appreciated that various concepts introduced
above and discussed in greater detail below may be implemented in
any of numerous ways, as the disclosed concepts are not limited to
any particular manner of implementation. Examples of specific
implementations and applications are provided primarily for
illustrative purposes. The exemplary methods, apparatus and systems
comprising the solutions platform can be used by an individual
(including a parent of an individual), a clinician, a physician,
and/or other medical or healthcare practitioner to provide data
that can be used for an assessment of the individual.
[0115] While an example is provided of the solutions platform
configured for ADHD, the solutions platform can also be configured
for other conditions, such as but not limited to depression,
bipolar depression, schizophrenia, or other condition as described
herein. In each of these conditions, the pertinent behavior
measures and symptom measures are configured in the solutions
platform for rating, and the data collected is analyzed to provide
useful measures.
[0116] Behaviors are a customized list that reflect the patient's
ability to perform a behavior. The list values and ability rankings
are as described herein, and allow for the customization. In a
non-limiting example, the behaviors measured using the solutions
platform can be based, at least in part, on one or more DSM-5
diagnostic and symptom criteria for a given disease or
condition.
[0117] The symptoms measured using the solutions platform can be
based, at least in part, on one or more symptoms on clinically
validated symptom lists that also reflect DSM-5 diagnostic and
symptom criteria for a given disease. Non-limiting examples include
the Vanderbilt assessment and follow up questionnaires, Vanderbilt
Assessment Follow-up for pediatric ADHD or the PHQ-9 for major
depressive disorder.
[0118] While the examples are described relative to behavior
measures or symptom measures, other types of measures are also
applicable to the solutions platform.
[0119] As described herein, the exemplary systems, methods, and
apparatus according to the principles herein can be implemented
using at least one processing unit of a programmed computing
device, to provide certain functionalities of the solutions
platform. FIG. 1 shows an exemplary apparatus 100 according to the
principles herein that can be used to implement certain
functionalities of the solutions platform including the analytics
engine (including classifier model) and report generator described
hereinabove herein. The example apparatus 100 includes at least one
memory 102 and at least one processing unit 104. The at least one
processing unit 104 is communicatively coupled to the at least one
memory 102.
[0120] Example memory 102 can include, but is not limited to,
hardware memory, non-transitory tangible media, magnetic storage
disks, optical disks, flash drives, computational device memory,
random access memory, such as but not limited to DRAM, SRAM, EDO
RAM, any other type of memory, or combinations thereof. Example
processing unit 104 can include, but is not limited to, a
microchip, a processor, a microprocessor, a special purpose
processor, an application specific integrated circuit, a
microcontroller, a field programmable gate array, any other
suitable processor, a graphical processing unit (GPU), or
combinations thereof.
[0121] The exemplary solutions platform can be configured to apply
a classifier model, using computational techniques and machine
learning tools, such as but not limited to linear/logistic
regression, principal component analysis, generalized linear mixed
models, random decision forests, support vector machines, or
artificial neural networks, to the collected data to create
composite variables or profiles that are more sensitive than each
measurement data value alone. For example, the analysis of the data
collected can be used to provide a measure of the pace of response
of an individual to a treatment, the likelihood of onset and/or
stage of progression of a condition (including a neurodegenerative
condition). In another example, the analysis of the data collected
can be used to provide a determination of the efficacy of
medication at controlling a behavior, or addressing a symptom of a
condition.
[0122] Any classification of an individual using the classifier
model as to the likelihood of onset and/or stage of progression of
a condition (including a neurodegenerative condition) according to
the principles herein can be transmitted as part of an enhanced
analysis report as a signal to a medical device, healthcare
computing system, or other device, and/or to a medical
practitioner, a health practitioner, a physical therapist, a
behavioral therapist, a sports medicine practitioner, a pharmacist,
or other practitioner, to allow formulation of a course of
treatment for the individual or to modify an existing course of
treatment, including to determine a change in dosage of a drug,
biologic or other pharmaceutical agent to the individual or to
determine an optimal type or combination of drug, biologic or other
pharmaceutical agent to the individual.
[0123] In an example, the classifier model can be trained using
data in a database that is collected in connection with individuals
who have exhibited desirable response times, to identify
correlations in the data. The identified correlations can help to
identify effective interventions, treatment efficacy, drug
performance, etc. in connection with the condition of interest.
[0124] In other examples, the computational models (including the
classifier models) may incorporate multiple features other than
just response time, such as but not limited to, various motor
functions, working memory accuracy, and other motor function
measurements and cognitive tasks.
[0125] In another example, the classifier model can be rules-based,
based on the type of conclusions that can be drawn based on a set
of values of the type of data collected in the implementation of
the solution platform.
[0126] In an example, a result of the application of the classifier
model to the data collected is an enhanced report that includes
suggested courses of action for a healthcare provider to evaluate.
The enhanced analysis report also can include data indicative of
the progress of the individual that a primary user specifies is to
be shared with the healthcare provider.
[0127] In any example, the classifier model can be trained using
training measurement data from subjects that are classified as to a
known likelihood of onset and/or stage of progression of a
condition or treatment responsiveness of a subject. In addition,
the exemplary classifier model can be further refined as to the
classification of individuals as to the desired classification. For
example, the classifier model can be trained based on data
indicating the progression over time of a set of symptoms, and be
applied to data from an unclassified individual to project how the
individual may be expected to response over time or projected
compliance with a therapy. For example, data may be collected based
on behavior measures for behaviors that correlate with poorer
compliance with a therapy or response to a treatment. The pattern
identified using the classifier model can be used to show the
potential effect of a certain modification to a treatment plan. For
example, behavior measures in the areas of the behaviors of
hyperactive, Inattentive, and impulsiveness can be by a classifier
model in the application directed to the treatment of ADHD.
[0128] In any example herein, the behavior measures and/or symptom
measures can be quantified using discrete settings, numerical
rating values, sliding scale quantifiers, or other measure that is
received as data to the solutions platform. In some examples, the
behavior measures and/or symptom measures can be quantified based
on a frequency or number of times of appearance of such symptom or
behavior (as applicable), of intensity of experience of such
symptom or behavior (as applicable).
[0129] In any example herein, the collected data from multiple
individuals can be analyzed (with authorization) to allow
population-based analysis to influence and inform treatment
practices. Such population-based analysis can allow for improved
health outcomes in a number of ways that could be superior to
existing treatments or other platforms. In an example, the
population-based analysis can be used to set the rating (or other
quantification scales) for the metrics measured using the
measurement fields (including the behavior measures and/or the
symptom measures), including for setting the threshold values or
discrete values of any of the rating scales, or to determine the
types of behavior and/or symptom or other measures that are more
sensitive predictors of outcome (whether good outcome or bad
outcome).
[0130] In any example herein, the data collected over time can be
analyzed to provide a measure of the individual's performance,
including as an individual, and also as compared to a population
group.
[0131] In any example herein, the individual need not be undergoing
a treatment or taking any medication in order to gain the benefit
of the solutions platform. For example, an individual can use the
solutions platform to monitor behaviors and symptoms for the
purpose of the user understanding the worsening or improvement of
the symptom(s) and/or behavior(s) of interest.
[0132] In FIG. 1, the at least one memory 102 is configured to
store processor-executable instructions 106 and a computing
component 108. In a non-limiting example, the computing component
108 can be used to analyze the data received and/or to generate the
enhanced report as described herein. As shown in FIG. 1, the memory
102 also can be used to store data 110, computation results from
application of at least one exemplary classifier model to the
received data, measurement data received at the measurement fields
(including one or more of a behavior measure and a symptom
measure), and/or data indicative of the response of an individual
to one or more treatments (including treatment using a cognitive
tool). As described herein, the plurality of measurement fields may
be rendered at a graphical user interface of a user device, and
data received at the measurement fields can be stored at the memory
102. In various examples, the data 110 can be received from one or
more measurement tools 112, such as but not limited to one or more
physiological or monitoring components and/or one or more
components configured for cognitive monitoring, assessment,
screening, and/or treatment.
[0133] While reference is made to a "measurement tool" in this and
other examples, it is to be understood that the measurement tool
may perform not merely measurements, but also to provide cognitive
and/or physiological measurements, monitoring, assessment,
screening, and/or treatment.
[0134] In various non-limiting examples, the measurement field can
include measurement parameters, such as but not limited to behavior
measures, symptom measures, medication and therapy compliance
measures (including type of medication, medication dosage levels,
medication use compliance measures), and/or quantifiable measures
of mood or state of mind. The measurement field may also allow
comments in prose form from the user/contributor (such as but not
limited to a diary-like entry field), to allow a user to comment on
an individual's status, progress, mood, or other parameter. For
example, the diary can be used by a user to indicate the
occurrences during a given time period for an individual, e.g.,
whether the impression is that treatments are progressing on
course, whether an individual is reacting somewhat poorly to a
treatment, an individual's mood, an individual's impulse behavior,
etc.
[0135] In a non-limiting example, the at least one processing unit
104 executes the processor-executable instructions 106 for the
analysis engine stored in the memory 102 at least to analyze the
data received in response to the measurement fields, using the
computing component 108. The at least one processing unit 104 also
can be configured to execute the processor-executable instructions
106 for the analysis engine stored in the memory 102 to analyze the
data from the one or more measurement tools 112 as described
herein, using the computing component 108. The at least one
processing unit 104 also can be configured to execute
processor-executable instructions 106 stored in the memory 102 to
apply an exemplary classifier model to the data received in
response to the measurement fields, to provide the analysis results
used at least in part to generate the enhanced analysis report. In
various examples, the enhanced analysis report can include
computation results indicative of the classification of an
individual according to status, and/or likelihood of onset, and/or
stage of progression of a condition, including as to a
neurodegenerative condition and/or an executive function disorder.
The at least one processing unit 104 also executes
processor-executable instructions 106 to control a transmission
unit to transmit values indicative of the analysis of the data
received in response to the measurement fields and/or data from the
measurement tool 112 as described herein, and/or control the memory
102 to store values indicative of the analysis of the data.
[0136] FIG. 2 is a block diagram of an exemplary network
environment suitable for a distributed implementation of the
solutions platform. The network environment can include one or more
servers 205 that are configured to communicate with user devices
203-I (I=1, 2, and 3) via a network 201. The solutions platform is
configured to provide control to user device 203-1 of user 1 to set
the user types and permission levels and access type associated
with each other user type in the group. For example, user device
203-1 can be used to send control signals that set a first set of
permission levels and access levels of user 1, a second (more
limited) set of permission levels and access levels for user type 2
associated with user device 203-2, and a third (restricted) set of
permission levels and access levels for user type 3 associated with
user device 203-3. In order to gain access to the exemplary
solutions platform, each user is required to be authenticated via
an authentication system 207. On receipt at the server(s) 205 of
the authentication of a given user type, the server 205 is
configured to grant access to the user type to the hosted
applications and/or content that are allowed based on the
permission levels set by the control signals from user device
203-1.
[0137] As used herein, the term "server" encompasses hardware
and/or software that provide the functionality described herein,
whether the particular functionality is embodied in a single
centralized configuration or is over a distributed configuration.
Each functionality of the server(s) described herein may be
performed using multiple intercommunicating computing systems or
using a single computing system programmed to perform different
server functionalities.
[0138] As will be appreciated, various distributed or centralized
configurations may be implemented, and in some embodiments a single
server can be used. Similarly, the user devices 203-I may be
incorporated into a single terminal.
[0139] While not shown in FIG. 2, the network environment may also
include one or more databases associated with server 205. In
various examples, the one or more databases can be used to store
user identifying information, the health information of one or more
individual(s), data indicative of the measures collected from the
user(s) (including behavior measures and/or symptom measures), or
other data described herein; while the server(s) 205 can store
analytics engines and/or report generating engines which can
implement one or more of the processes described herein. The
exemplary analytics engine can be used to apply an exemplary
classifier model to the data received in response to the
measurement fields, to provide the analysis results used at least
in part to generate the enhanced analysis report.
[0140] An electronic display device (not shown) associated with
user device 203-1 may display a rendered graphical user interface
(GUI) to a user as described herein. Once the display device
receives instructions from the server 205, the GUI may be rendered
to allow an individual to interact with the servers to implement
processes described herein, including defining the other user
types, setting the permission levels and access levels of the
defined user types, displaying fields for entering measurement
data, and receiving the measurement data, as described herein.
[0141] The exemplary network 201 may include, but is not limited
to, the Internet, an intranet, a LAN (Local Area Network), a WAN
(Wide Area Network), a MAN (Metropolitan Area Network), a wireless
network, an optical network, and the like. In various examples, the
user device 203-1 is in communication with the server 205 and
database(s) and can generate and transmit database queries
requesting information from the raw data matrices or database(s).
The server 205 can transmit instructions to the user device 203-1
over the network 401. The server 205 can interact with the user
device 203-1 and database(s) over network 401 to render the GUI on
the user device 203-1, as described herein.
[0142] A user device 203-I may include, but is not limited to, one
or more of work stations, computers, general purpose computers,
Internet appliances, hand-held devices, wireless devices, portable
devices, wearable computers, cellular or mobile phones, portable
digital assistants (PDAs), smart phones, tablets, ultrabooks,
netbooks, laptops, desktops, multi-processor systems,
microprocessor-based or programmable consumer electronics, game
consoles, set-top boxes, network PCs, mini-computers, smartphones,
tablets, netbooks, and the like. The user devices 203-I may include
some or all components described in relation to computing device(s)
described herein (including as shown in FIGS. 2-6).
[0143] Any user device 203-I may connect to network 401 via a wired
or wireless connection. The user device 203-I may include one or
more applications such as, but not limited to, a web browser and
the like. In an exemplary embodiment, the user device 203-I may
perform all the functionalities described herein.
[0144] In other embodiments, the server(s) 205 performs the
functionalities described herein. In yet another embodiment, the
user device 203-I may perform some of the functionalities, and
server(s) 205 performs the other functionalities described
herein.
[0145] Each of the databases (not shown), and servers 205 may be
connected to the network 201 via a wired connection. Alternatively,
one or more of the databases (not shown) and servers 205 may be
connected to the network 201 via a wireless connection. Although
not shown, server 205 can be (directly) connected to the
database(s). Servers 205 comprises one or more computers or
processors configured to communicate with user device 203-I via
network 201. Servers 205 hosts one or more applications or websites
accessed by user device 203-I and/or facilitates access to the
content of the database(s). Servers 205 may include one or more
components described in relation to system 100 shown in FIG. 1. The
database(s) include one or more storage devices for storing data
and/or instructions (or code) for use by servers 205, and/or user
device 203-I. The database(s) and/or servers 205, may be located at
one or more geographically distributed locations from each other or
from user device 203-I. Alternatively, the database(s) may be
included within servers 205.
[0146] FIG. 3 is a block diagram of another exemplary network
environment suitable for a distributed implementation of the
solutions platform. The description provided herein in connection
with the features and functionalities of components of FIG. 2 also
apply to equivalent components of FIG. 3. The exemplary network
environment can include one or more servers 305 that are configured
to communicates with user devices 303-I (I=1, 2, and 3) and a
measurement tool 309 via a network 301. The measurement tool can
be, but is not limited to, one or more physiological or monitoring
components and/or one or more components configured for cognitive
monitoring, assessment, screening, and/or treatment. The solutions
platform is configured to provide control to user device 303-1 of
user 1 to set the user types and permission levels and access type
associated with each other user type in the group, and to specify
the source and the type of data to be received from the one or more
measurement tools 309. In this example, measurement tool 309 may be
configured to transmit (wired or wirelessly) data or other
information to the solutions platform, or may not be coupled to the
solutions platform (but rather, the solutions platform is
configured to display measurement fields that request input of the
data resulting from the measurements of the measurement tool). For
example, user device 303-1 can be used to send control signals that
set a first set of permission levels and access levels of user 1, a
second (more limited) set of permission levels and access levels
for user type 2 associated with user device 303-2, and a third
(restricted) set of permission levels and access levels for user
type 3 associated with user device 303-3. In order to gain access
to the exemplary solutions platform, each user is required to be
authenticated via an authentication system 307. On receipt at the
server(s) 305 of the authentication of a given user type, the
server 305 is configured to grant access to the user type to the
hosted applications and/or content that are allowed based on the
permission levels set by the control signals from user device
303-1.
[0147] While not shown in FIG. 3, the network environment may also
include one or more databases associated with server 305. In
various examples, the one or more databases can be used to store
user identifying information, the health information of one or more
individual(s), data indicative of the measures collected from the
user(s) (including behavior measures and/or symptom measures), data
from the one or more measurement tools 309, or other data described
herein; while the server(s) 305 can store analytics engines and/or
report generating engines which can implement one or more of the
processes described herein. The exemplary analytics engine can be
used to apply an exemplary classifier model to the data received in
response to the measurement fields, to provide the analysis results
used at least in part to generate the enhanced analysis report.
[0148] An electronic display device (not shown) associated with
user device 303-1 may display a rendered graphical user interface
(GUI) to a user as described herein. Once the display device
receives instructions from the server 305, the GUI may be rendered
to allow an individual to interact with the servers to implement
processes described herein, including defining the other user
types, setting the permission levels and access levels of the
defined user types, displaying fields for entering measurement
data, specifying the source and the type of data to be received
from the one or more measurement tools 309, and receiving the
measurement data, as described herein.
[0149] FIG. 4 is a block diagram of yet another exemplary network
environment suitable for a distributed implementation of the
solutions platform. The description provided herein in connection
with the features and functionalities of components of FIGS. 2 and
3 also apply to equivalent components of FIG. 4. The exemplary
network environment can include one or more servers (configured to
function as analytics engines 411 and a gateway 413) and databases
415-421 that are configured to communicates with user devices 403-I
(I=1, 2, and 3) via a network 401. One or more measurement tools
(not shown), such as but is not limited to, one or more
physiological or monitoring components and/or one or more
components configured for cognitive monitoring, assessment,
screening, and/or treatment, may communicate data via a network 401
to the one or more servers (configured to function as analytics
engines 411 and a gateway 413) and databases 415-421. In this
example, the measurement tool may be configured to transmit (wired
or wirelessly) data or other information to the solutions platform,
or may not be coupled to the solutions platform (but rather, the
solutions platform is configured to display measurement fields that
request input of the data resulting from the measurements of the
measurement tool).
[0150] The exemplary solutions platform is configured to provide
control to user device 403-1 of user 1 to set the user types and
permission levels and access type associated with each other user
type in the group, and to specify the source and the type of data
to be received from the one or more measurement tools. For example,
user device 403-1 can be used to send control signals that set a
first set of permission levels and access levels of user 1, a
second (more limited) set of permission levels and access levels
for user type 2 associated with user device 403-2, and a third
(restricted) set of permission levels and access levels for user
type 3 associated with user device 403-3. In order to gain access
to the exemplary solutions platform, each user is required to be
authenticated via an authentication system 407. On receipt at the
server(s) (configured to function as analytics engines 411 and a
gateway 413) of the authentication of a given user type, the
server(s) (configured to function as analytics engines 411 and a
gateway 413) is configured to grant access to the user type to the
hosted applications and/or content that are allowed based on the
permission levels set by the control signals from user device
403-1.
[0151] As shown in FIG. 4, the network environment includes one or
more databases 415-421 associated with the one or more servers
(configured to function as analytics engines 411 and a gateway
413). In various examples, the one or more databases 415-421 can be
used to store user identifying information, the health information
of one or more individual(s), data indicative of the measures
collected from the user(s) (including behavior measures and/or
symptom measures), data from the one or more measurement tools, or
other data described herein; while the server(s) (configured to
function as analytics engines 411 and a gateway 413) can store
analytics engines and/or report generating engines which can
implement one or more of the processes described herein. In the
non-limiting example of FIG. 4, database 415 is used to store usage
analytics (generated using the analytics engine); database 417 is
used to store the preferences set using control signals from the
user 1 device (such as but not limited to reminder frequency and
the specified behaviors to be quantified and tracked); database 419
is used to store accounts and profile information for each of the
users and user types identified based on control signals from the
user device 1 (such as but not limited to data indicative of
identifying information (ID) of the individual whose health
information is being analyzed (patient ID), the ID of the
individual's teacher(s), and the individual's caregivers); and
database 421 is used to store data indicative of health information
(such as but not limited to data indicative of the individual's
behavior measures, symptom measures, compliance level, and other
measurements (including treatment telemetry)).
[0152] The exemplary analytics engine 411 can be used to apply an
exemplary classifier model to the data received in response to the
measurement fields, to provide the analysis results used at least
in part to generate the enhanced analysis report.
[0153] As shown in FIG. 4, the exemplary gateway 413 controls the
communications between the network 401 and database 417
(preferences set using control signals from the user 1 device),
database 419 (which includes identifying information) and database
421 (which includes health data). The exemplary gateway 413 also
communicates with the authentication system 407. Accordingly, based
on the control signals from the user device 1 (403-1), the gateway
413 is configured to control the permissions and access levels of
each user type, to determine the authentication state of any user,
and to control the granting of access of an authenticated user. The
gateway 413 is also configured to apply an encryption protocol
(including TLS version 1.x, implementing (as a non-limiting
example) a Cipher of AES256 encryption protocol) to encrypt or
decrypt data being exchanged between the user devices, the servers,
and the databases. The encrypted data resulting from the
application of an encryption protocol can be shared via gateway 413
across the distributed environment more securely, thereby providing
additional security for the secured collection, analysis, and
sharing of data across multiple distributed data assets (including
across multiple distributed platforms). The gateway 413 also can be
configured, based on the control signals from the user device 1
(403-1), to control and verify the access levels of each user that
attempts to exchange data over network 401.
[0154] An electronic display device (not shown) associated with
user device 403-1 may display a rendered graphical user interface
(GUI) to a user as described herein. Once the display device
receives instructions from the server(s) (configured to function as
analytics engines 411 and a gateway 413), the GUI may be rendered
to allow an individual to interact with the servers to implement
processes described herein, including defining the other user
types, setting the permission levels and access levels of the
defined user types, displaying fields for entering measurement
data, specifying the source and the type of data to be received
from the one or more measurement tools 409, and receiving the
measurement data, as described herein.
[0155] FIG. 5A is a block diagram of yet another exemplary network
environment suitable for a distributed implementation of the
solutions platform. The description provided herein in connection
with the features and functionalities of components of FIGS. 2, 3,
and 4 also apply to equivalent components of FIG. 5A. The exemplary
network environment can include one or more servers (configured to
function as analytics engines 511 and a gateway 513) and databases
515-521 that are configured to communicates with user devices 503-I
(I=1, 2, and 3) via a network 501. One or more measurement tools
509, such as but is not limited to, one or more physiological or
monitoring components and/or one or more components configured for
cognitive monitoring, assessment, screening, and/or treatment, may
communicate data via a network 501 to the one or more servers
(configured to function as analytics engines 511 and a gateway 513)
and databases 515-521. In this example, measurement tool 509 may be
configured to transmit (wired or wirelessly) data or other
information to the solutions platform, or may not be coupled to the
solutions platform (but rather, the solutions platform is
configured to display measurement fields that request input of the
data resulting from the measurements of the measurement tool). As
shown in the non-limiting example of FIG. 5A, an analysis engine
523 running a classifier model may be used to analyze the data in
database 521. As a non-limiting example, analysis engine 523 may be
configured to implement a machine learning tool.
[0156] The exemplary solutions platform is configured to provide
control to user device 503-1 of user 1 to set the user types and
permission levels and access type associated with each other user
type in the group, and to specify the source and the type of data
to be received from the one or more measurement tools 509. For
example, user device 503-1 can be used to send control signals that
set a first set of permission levels and access levels of user 1, a
second (more limited) set of permission levels and access levels
for user type 2 associated with user device 503-2, and a third
(restricted) set of permission levels and access levels for user
type 3 associated with user device 503-3. In order to gain access
to the exemplary solutions platform, each user is required to be
authenticated via an authentication system 507. On receipt at the
server(s) (configured to function as analytics engines 511 and a
gateway 513) of the authentication of a given user type, the
server(s) (configured to function as analytics engines 511 and a
gateway 513) is configured to grant access to the user type to the
hosted applications and/or content that are allowed based on the
permission levels set by the control signals from user device
503-1.
[0157] The exemplary network environment is configured to receive
and transmit data received from different types of user devices,
including user devices 503-I, based on control signals from user
device 503-1. For example, the control signals from user device
user device 503-1 can provide identifying data of the other allowed
user types (such as but not limited to patient ID, teacher ID, and
caregiver ID), the permission and access levels restrictions for
each user type, reminder frequencies, and the type of behaviors to
be measured by each user type. In this non-limiting example, the
control signal specifies that user type 2 is permitted to provide
only data indicative of the behavior measures and symptom measures
for the individual. In this example, the control signals from user
device 1 can cause the gateway servers to assign the classification
of user type 2 only to select teachers and/or select other
caregivers, thereby causing the gateway to provide instructions for
the user device 503-2 to display to (or otherwise provide) the
allowed measurement fields to the select teachers and/or select
other caregivers and to receive only the data indicative of the
behavior measures and symptom measures for the individual in
response to the measurement field provided at the user devices
503-2. In this non-limiting example, the control signal also
specifies that user type 3 is permitted only to receive the
enhanced analysis report(s) that is generated based at least in
part on an analysis engine applied to the date from the measurement
fields provided to the user devices 503-1 and 503-2, and other data
provided at user device 503-1. In this example, the control signals
from user device 1 can cause the gateway servers to assign the
classification of user type 3 only to select caregivers (including
select healthcare provider), thereby causing the gateway to allow
the select teachers and/or select other caregivers to provide only
the data indicative of the behavior measures and symptom measures
for the individual in response to the measurement field provided at
the user devices 503-2.
[0158] The exemplary analytics engine 511 can be used to apply an
exemplary classifier model to the data received in response to the
measurement fields, to provide the analysis results used at least
in part to generate the enhanced analysis report.
[0159] As shown in FIG. 5A, the measurement tool can be used to
provide data indicative of the individual's results from
interacting with the measurement tool, including treatment (or
therapy) telemetry and/or compliance. As described hereinabove, the
measurement tool can be configured for cognitive monitoring,
assessment, screening, and/or treatment.
[0160] As shown in FIG. 5A, the network environment includes one or
more databases 515-521 associated with the one or more servers
(configured to function as analytics engines 511 and a gateway
513). In various examples, the one or more databases 515-521 can be
used to store user identifying information, the health information
of one or more individual(s), data indicative of the measures
collected from the user(s) (including behavior measures and/or
symptom measures), data from the one or more measurement tools 509,
or other data described herein; while the server(s) (configured to
function as analytics engines 511 and a gateway 513) can store
analytics engines and/or report generating engines which can
implement one or more of the processes described herein. In the
non-limiting example of FIG. 5A, database 515 is used to store
usage analytics (generated using the analytics engine); database
517 is used to store the preferences set using control signals from
the user 1 device (such as but not limited to reminder frequency
and the specified behaviors to be quantified and tracked); database
519 is used to store accounts and profile information for each of
the users and user types identified based on control signals from
the user device 1 (such as but not limited to data indicative of
identifying information (ID) of the individual whose health
information is being analyzed (patient ID), the ID of the
individual's teacher(s), and the individual's caregivers); and
database 521 is used to store data indicative of health information
(such as but not limited to data indicative of the individual's
behavior measures, symptom measures, compliance level, and other
measurements (including treatment telemetry)).
[0161] The exemplary gateway 513 controls the communications
between the network 501 and database 517 (preferences set using
control signals from the user 1 device), database 519 (which
includes identifying information) and database 521 (which includes
health data). The exemplary gateway 513 also communicates with the
authentication system 507. Accordingly, based on the control
signals from the user device 1 (503-1), the gateway 513 is
configured to control the permissions and access levels of each
user type, to determine the authentication state of any user, and
to control the granting of access of an authenticated user. The
gateway 513 is also configured to apply an encryption protocol
(including TLS version 1.x, implementing (as a non-limiting
example) a Cipher of AES256 encryption protocol) to encrypt or
decrypt data being exchanged between the user devices, the servers,
and the databases. The encrypted data resulting from the
application of an encryption protocol can be shared via gateway 513
across the distributed environment more securely, thereby providing
additional security for the secured collection, analysis, and
sharing of data across multiple distributed data assets (including
across multiple distributed platforms). The gateway 513 also can be
configured, based on the control signals from the user device 1
(503-1), to control and verify the access levels of each user that
attempts to exchange data over network 501.
[0162] In another example of FIG. 5A, the communication between the
one or more servers (including a server configured to execute the
analytics engine) and the user device 503-1 can be through a secure
push notification process. In other non-limiting examples, the
gateway 513 can be configured to communicate with one or more of
user device 503-2 and user device 503-3 through a secure push
notification, based on control signals from the user device
503-1.
[0163] An electronic display device (not shown) associated with
user device 503-1 may display a rendered graphical user interface
(GUI) to a user as described herein. Once the display device
receives instructions from the server(s) (configured to function as
analytics engines 511 and a gateway 513), the GUI may be rendered
to allow an individual to interact with the servers to implement
processes described herein, including defining the other user
types, setting the permission levels and access levels of the
defined user types, displaying fields for entering measurement
data, specifying the source and the type of data to be received
from the one or more measurement tools 509, and receiving the
measurement data, as described herein.
[0164] In some examples, the communications between the one or more
server and the user devices (including user 1, user 2, or user 3)
can be effected using secure links that are set up over an email
service.
[0165] In any example described herein, the communications between
the one or more server and the user devices (including user 1, user
2, or user 3) can be effected via a secure push notification set
up. For example, the user 1 user device can be used to set
preferences for the reminders and notifications that go to user 2
types (including teachers and healthcare providers) via a secure
push notification to a mobile device.
[0166] FIG. 5B is a block diagram of yet another exemplary network
environment suitable for a distributed implementation of the
solutions platform. The description provided herein in connection
with the features and functionalities of components of FIGS. 2, 3,
4, and 5A also apply to equivalent components of FIG. 5B, but with
the added component of a content module 525 that is in
communication with gateway 513, database 521, and analysis engine
523. The content module 525 is configured to generate one or more
content queries based at least in part on the analysis results from
the (such as but not limited to behavior assessment data), thereby
providing for contextual and predictive content targeting.
[0167] Exemplary inputs to the content module 525 are audience
(e.g., age or age range, user), context (time, activity, location),
domain (symptom, behavior, impairment), and rating(s). The content
module 525 may include a content targeting engine configured to
implement a rules engine for, e.g., converting raw telemetry from
tracking or treatments into the contextual and domain inputs. The
predictive content targeting may be based on machine learning via
patterns or predicted trends in combination with one or more inputs
(i.e., audience, context, domain, and/or ratings inputs). Content
engagement may be tracked based on content that was viewed, read,
and/or favored by the users, as a feedback loop for future content
generation.
[0168] As an example of predictive content modeling, users may be
designated to fall into defined categories called "profiles" based
on a variety of dimensions, e.g., app usage, tracking data,
environment, and role. Content may have meta data "tags" associated
with a taxonomy that matches the data collected. Algorithms may
determine what content to show to the user, based upon the profile
of the user.
[0169] As a non-limiting example, the content module 525 can be
configured to communicate the one or more content queries to one or
more content libraries 527 having APIs and which communicate with
at least one content index 529. The queries can be constructed
based on the results of the analysis from the analysis engine 523
to identify educational, informational, clinical, behavioral, or
other type of content to output to the user of the system and/or to
the individual whose symptom and behaviors are being measured. The
content queries can be targeted to identify content that may assist
the individual improve a scoring of one or more symptoms measured
based on the individual's condition or to improve and/or modify one
or more behaviors exhibited by the individual and which are being
measured based on the condition.
[0170] As non-limiting examples, the content library can be a
national resource database, a medical society database, a
professional society database, or a privately-curated library, or
any other source that can be queried to provide relevant content.
For example, where the condition is ADHD, the content library can
be affiliated with the CHADD national resource (a recognized
authority on ADHD).
[0171] As a non-limiting example, for an individual having ADHD,
one or more symptoms of ADHD and one or more associated behaviors
are analyzed to determine one or more of: (i) a pace of response of
the individual to a treatment, (ii) a status of the condition,
(iii) an efficacy of medication at controlling a behavior, or (iv)
an efficacy of medication at addressing a symptom of the condition.
The queries generated based on the analysis can be targeted to
identify content that may assist the individual and/or at least one
caregiver of the individual to modify (including to improve) a
scoring on at least one symptom measure or to modify (including to
improve) a scoring on at least one behavior measure. In this
example, the content query may be targeted to identify content that
may assist an individual hone homework or test-taking skills where
the analysis module shows based on the analysis that that
individual's capabilities are not improving with the other
treatment the individual is receiving.
[0172] As another non-limiting example, for an individual having
major depressive disorder (MDD), one or more symptoms of MDD and
one or more associated behaviors are analyzed to determine one or
more of: (i) a pace of response of the individual to a treatment,
(ii) a status of the condition, (iii) an efficacy of medication at
controlling a behavior, or (iv) an efficacy of medication at
addressing a symptom of the condition. The queries generated based
on the analysis can be targeted to identify content that may assist
the individual and/or at least one caregiver of the individual to
modify (including to improve) a scoring on at least one symptom
measure or to modify (including to improve) a scoring on at least
one behavior measure. In this example, the content query may be
targeted to identify content that may assist an individual address
a cognitive deficit attendant to the MDD, where the analysis module
shows based on the analysis that that individual's cognitive
abilities are not improving with the other treatment the individual
is receiving.
[0173] As a specific example of content targeting, a care giver may
select and indicate on a behavior tracking form that his/her child
"not at all" and "quite a bit" holds one of four specific behaviors
over the last seven days. The system may show the parent, e.g., on
user device 1, health tips that may help the parent at home and at
school in the following use cases: [0174] 1. Specific parent facing
home-morning routine [0175] 2. Specific parent facing home-homework
[0176] 3. Specific parent facing school [0177] 4. Generic parent
home [0178] 5. Generic parent school
[0179] FIG. 6 is a block diagram of an exemplary computing device
610 that can be used as a computing component to perform one or
more of the procedures described herein, including in connection
with FIGS. 1-4. In any example herein, computing device 610 can be
configured as a console that receives user input to implement the
computing component, including to perform one or more of the
analyses and/or to generate the one or more enhanced analysis
reports. For clarity, FIG. 6 also refers back to and provides
greater detail regarding various elements of the exemplary system
of FIG. 1. The computing device 610 can include one or more
non-transitory computer-readable media for storing one or more
computer-executable instructions or software for implementing
examples. The non-transitory computer-readable media can include,
but are not limited to, one or more types of hardware memory,
non-transitory tangible media (for example, one or more magnetic
storage disks, one or more optical disks, one or more flash
drives), and the like. For example, memory 102 included in the
computing device 610 can store computer-readable and
computer-executable instructions or software for performing the
operations disclosed herein. For example, the memory 102 can store
a software application 650 which is configured to perform various
of the disclosed operations (e.g., analyze data received in
connection with the one or more measurement fields, applying an
exemplary classifier model to the data, performing a computation to
analyze the data, or generate the enhanced analysis reports). The
computing device 610 also includes configurable and/or programmable
processor 104 and an associated core 614, and optionally, one or
more additional configurable and/or programmable processing
devices, e.g., processor(s) 612' and associated core(s) 614' (for
example, in the case of computational devices having multiple
processors/cores), for executing computer-readable and
computer-executable instructions or software stored in the memory
102 and other programs for controlling system hardware. Processor
104 and processor(s) 612' can each be a single core processor or
multiple core (614 and 614') processor.
[0180] Virtualization can be employed in the computing device 610
so that infrastructure and resources in the console can be shared
dynamically. A virtual machine 624 can be provided to handle a
process running on multiple processors so that the process appears
to be using only one computing resource rather than multiple
computing resources. Multiple virtual machines can also be used
with one processor.
[0181] Memory 102 can include a computational device memory or
random access memory, such as but not limited to DRAM, SRAM, EDO
RAM, and the like. Memory 102 can include a non-volatile memory,
such as but not limited to a hard-disk or flash memory. Memory 102
can include other types of memory as well, or combinations
thereof.
[0182] In a non-limiting example, the memory 102 and at least one
processing unit 104 can be components of a peripheral device, such
as but not limited to a dongle (including an adapter) or other
peripheral hardware. The exemplary peripheral device can be
programmed to communicate with or otherwise couple to a primary
computing device, to provide the functionality of any of the
exemplary measurement tools, apply an exemplary classifier model,
and implement any of the exemplary analyses (including the
associated computations) described herein. In some examples, the
peripheral device can be programmed to directly communicate with or
otherwise couple to the primary computing device (such as but not
limited to via a USB or HDMI input), or indirectly via a cable
(including a coaxial cable), copper wire (including, but not
limited to, PSTN, ISDN, and DSL), optical fiber, or other connector
or adapter. In another example, the peripheral device can be
programmed to communicate wirelessly (such as but not limited to
Wi-Fi or Bluetooth.RTM.) with primary computing device. The
exemplary primary computing device can be a smartphone (such as but
not limited to an iPhone.RTM., a BlackBerry.RTM., or an
Android.TM.-based smartphone), a television, a workstation, a
desktop computer, a laptop, a tablet, a slate, an electronic-reader
(e-reader), a digital assistant, or other electronic reader or
hand-held, portable, or wearable computing device, or any other
equivalent device, an Xbox.RTM., a Wii.RTM., or other equivalent
form of computing device.
[0183] A user can interact with the computing device 610 through a
visual display unit 628, such as a computer monitor, which can
display one or more rendered graphical user interfaces 630 that can
be provided in accordance with exemplary systems and methods. The
computing device 610 can include other I/O devices for receiving
input from a user, for example, a keyboard or any suitable
multi-point touch interface 618, a pointing device 620 (e.g., a
mouse), a camera or other image recording device, a microphone or
other sound recording device, an accelerometer, a gyroscope, a
sensor for tactile, vibrational, or auditory signal, and/or at
least one actuator. The keyboard 618 and the pointing device 620
can be coupled to the visual display unit 628. The computing device
610 can include other suitable conventional I/O peripherals.
[0184] The computing device 610 can also include one or more
storage devices 634 (including a single core processor or multiple
core processor 636), such as a hard-drive, CD-ROM, or other
computer readable media, for storing data and computer-readable
instructions and/or software that perform operations disclosed
herein. Exemplary storage device 634 (including a single core
processor or multiple core processor 636) can also store one or
more databases for storing any suitable information required to
implement exemplary systems and methods. The databases can be
updated manually or automatically at any suitable time to add,
delete, and/or update one or more items in the databases.
[0185] The computing device 610 can include a network interface 622
configured to interface via one or more network devices 632 with
one or more networks, for example, Local Area Network (LAN),
metropolitan area network (MAN), Wide Area Network (WAN) or the
Internet through a variety of connections including, but not
limited to, standard telephone lines, LAN or WAN links (for
example, 802.11, T1, T3, 56 kb, X.25), broadband connections (for
example, ISDN, Frame Relay, ATM), wireless connections, controller
area network (CAN), or some combination of any or all of the above.
The network interface 622 can include a built-in network adapter,
network interface card, PCMCIA network card, card bus network
adapter, wireless network adapter, USB network adapter, modem or
any other device suitable for interfacing the computing device 610
to any type of network capable of communication and performing the
operations described herein. Moreover, the computing device 610 can
be any computational device, such as a smartphone (such as but not
limited to an iPhone.RTM., a BlackBerry.RTM., or an
Android.TM.-based smartphone), a television, a workstation, a
desktop computer, a server, a laptop, a tablet, a slate, an
electronic-reader (e-reader), a digital assistant, or other
electronic reader or hand-held, portable, or wearable computing
device, or any other equivalent device, an Xbox.RTM., a Wii.RTM.,
or other equivalent form of computing or telecommunications device
that is capable of communication and that has or can be coupled to
sufficient processor power and memory capacity to perform the
operations described herein. The one or more network devices 632
may communicate using different types of protocols, such as but not
limited to WAP (Wireless Application Protocol), TCP/IP
(Transmission Control Protocol/Internet Protocol), NetBEUI (NetBIOS
Extended User Interface), or IPX/SPX (Internetwork Packet
Exchange/Sequenced Packet Exchange).
[0186] The computing device 610 can run any operating system 626,
such as any of the versions of the Microsoft.RTM. Windows.RTM.
operating systems, iOS.RTM. operating system, Android.TM. operating
system, the different releases of the Unix and Linux operating
systems, any version of the MacOS.RTM. for Macintosh computers, any
embedded operating system, any real-time operating system, any open
source operating system, any proprietary operating system, or any
other operating system capable of running on the console and
performing the operations described herein. In some examples, the
operating system 626 can be run in native mode or emulated mode. In
an example, the operating system 626 can be run on one or more
cloud machine instances.
[0187] FIG. 7 shows a flowchart of a non-limiting exemplary method
that can be implemented using any solutions platform described
herein that executes processor-executable instructions using at
least one server. In block 702, in response to authentication of a
first user using an authentication system, the server provides a
first access means of a first user device to a first plurality of
measurement fields, the first plurality of measurement fields
comprising one or more of a behavior measure and a symptom measure.
In block 704, the server is used to receive first data from the
first user device in connection with the first plurality of
measurement fields. In block 706, in response to authentication
using the authentication system of a second user as belonging to a
second user type, the server is used to provide a second access
means of a second user device to a second plurality of measurement
fields, where the second plurality of measurement fields is
configured based on at least one control signal from the first user
device, and the second plurality of measurement fields differing
from the first plurality of measurement fields by one or more of a
behavior measure and a symptom measure. In block 708, the server is
used to receive second data from the second user device in
connection with the second plurality of measurement fields. In
block 710, the server is used to cause an analytics engine to
perform the computational analysis of the first data and/or the
second data. In block 712, the server is used to cause a reporting
module to generate an enhanced analysis report using an output from
the analytics engine. In block 714, in response to authentication
using the authentication system of a third user as belonging to a
third user type, the server is used to provide a third access means
of the third user to provide access to the enhanced analysis
report.
[0188] FIGS. 8A-8B show a flowchart of another non-limiting example
method that can be implemented using a solutions platform that
includes at least one processing unit and at least one server.
Operations of the at least one processing unit is described in
connection with FIG. 8A as follows. In block 802, the at least one
processing unit Is used to receive a first authentication of a
first user using an authentication system. In block 804, based on
control signals from the first user device, the at least one
processing unit is used to associate with the first user a first
plurality of measurement fields comprising one or more of a
behavior measure and a symptom measure. In block 806, based on
control signals from the first user device, the at least one
processing unit is used to specify a second user type and a third
user type. In block 808, based on control signals from the first
user device, the at least one processing unit is used to associate
a second plurality of measurement fields with the second user type,
the second plurality of measurement fields being configured based
on at least one control signal from the first user device, and the
second plurality of measurement fields differing from the first
plurality of measurement fields by one or more of a behavior
measure and a symptom measure. In block 810, based on control
signals from the first user device, the at least one processing
unit is used to associate an enhanced analysis report with the
third user type, the enhanced analysis report comprising a
computational enhancement of first data received in connection with
the first plurality of measurement fields and/or second data
received in connection with the second plurality of measurement
fields. Operations of the at least one server is described in
connection with FIG. 8B as follows. In block 822, in response to
the first authentication of the first user using the authentication
system, the at least one server is used to configure a first access
means of the first user device to the first plurality of
measurement fields. In block 824, the at least one server is used
to configure a second access means of the second user type to the
second plurality of measurement fields. In block 826, the at least
one server is used to cause an analytics engine to perform the
computational analysis of the first data and/or the second data. In
block 828, the at least one server is used to cause to cause a
reporting module to generate the enhanced analysis report using an
output from the analytics engine. In block 830, the at least one
server is used to configure a third access means of the third user
type to provide access to the enhanced analysis report.
[0189] FIG. 9 is a flow diagram showing an example of the types of
permissions that can be set on the solutions platform based on the
control signals set by user 1 902. As described herein, user 1 may
be an individual patient, a group of patients, or someone acting on
behalf of the patient (parent, custodian, guardian, or other
consented individual), particularly if the patient is a child
(including a minor child). Based on the user 1 set controls input,
the solutions platform sets the permission levels and access types
for those designated as user type 2, which can include caregivers
904 and/or teachers 906, and user type 3, which can include
physicians 906. The solutions platform also provides user 1 902
with the capability to set the permissions for any onward transfer
of or access to data or other information, or at least a portion of
the enhanced analysis report. For example, as shown in FIG. 9,
solutions platform also provides user 1 with the capability to
configure the permission and access levels for user type 3 906 to
allow any onward transfer of or access to data or other information
or at least a portion of the enhanced analysis report to one or
more others 908, such as but not limited to office staff of the
user type 3. As also shown in FIG. 9, solutions platform also
provides user 1 with the capability to configure the permission and
access levels for user type 2 906 to allow any onward transfer of
or access to data or other information or at least a portion of the
enhanced analysis report to one or more others 910, such as but not
limited to entities for insurance reimbursement (including
payers).
[0190] As a non-limiting example, the solutions platform can be
configured such that a user type 3 (e.g., a physicians) can use the
data collected and/or the enhanced analysis report to provide
remote healthcare. In this example, the permission and access
levels set by user 1 can create settings such that the data
collected and/or the enhanced analysis report provided to a user
type 3 meets requirements for billing and compensation (or other
type of reimbursement), e.g. using CPT codes. In this example, the
enhanced analysis report includes descriptions of healthcare
provider activities, and/or diagnoses, and/or patient data or
progress reports, and associated metrics and scales, which meet the
requirement for reimbursement under a given desired CPT code.
[0191] FIG. 10 shows a flowchart of an exemplary use of the
solutions platform by user 1 to set permission levels and access
types and the type of data and other information that user 1 is
given the capability to enter at rendered graphical user
interface(s). In this non-limiting example, user 1 is acting on
behalf of a child patient. The solutions platform presents an
account page that user 1 can navigate to or otherwise access, such
as but not limited to by launching an App on a mobile device, a
tablet, or other computing device as described herein. If user 1
has already set up an account, the solutions platform presents user
1 with a rendered graphical user interface that is a login window
for user 1's login credentials. If user 1 has no account, the
solutions platform presents user 1 with rendered graphical user
interfaces to facilitate creation of the account and identification
of the individual to be monitored, whether it is user 1 or another
individual (such as but not limited to a child, including a minor
child). User 1 is provided with the rendered graphical user
interfaces to configure the solutions platform by selecting the
behavior categories and from that the behavior selections to be
measured and quantified, User 1 is also provided with the rendered
graphical user interfaces to configure the type of behavior
tracking confirmation. The solutions platform also presents user 1
with rendered graphical user interfaces for setting type of
reminders, the frequency and manner of sending of the reminders to
user 1 and other users (user type 2 and/or user type 3). User 1 is
also provided with the rendered graphical user interfaces to
specify the identity and permissions and access levels for user
types 2 and 3. For example, as shown in the example of FIG. 10, the
user type 2 can be provided as a teacher list. User 1 can set the
type of reminders, the frequency and manner of sending of the
reminders to user type 2. User 1 is also presented rendered
graphical user interfaces to retrieve the information for the user
types from other user 1 accounts or device settings, such as but
not limited to, user 1 contacts.
[0192] FIGS. 11A-11D show non-limiting examples of the types of
data and other information that can be included in an enhanced
analysis report. FIG. 11A shows an example of an analysis
indicating an individual's compliance with the set requirements of
a given treatment or other regimen (including frequency of taking a
medication or other treatment, or the dosage level of medication
taken). FIG. 11B shows an example of the type of symptom measures
that can be quantified or reported, including an indication of the
symptoms that may never appear, symptoms that occasionally appear,
or symptoms that appear often or very often, based on a rating
scale set in the solutions platform. FIG. 11B also shows the type
of computational analysis, visualizations, and the graph plots
(e.g., actual and/or projected frequency and/or intensity of
symptom appearance over time) that can be generated based on the
data collected from user 1, user type 2, and user type 3. FIG. 11C
shows an example of the type of behavior measures that can be
quantified or reported, including an indication of the behaviors
that are quantified as appearing above or below average based on a
rating scale set in the solutions platform. FIG. 11C also shows the
type of computational analysis, visualizations, and the graph plots
(e.g., actual and/or projected frequency and/or intensity of
behavior appearance over time) that can be generated based on the
data collected from user 1, user type 2, and user type 3. FIG. 11D
shows an example of a measure from a measurement tool (in this
non-limiting example, a cognitive tool) that user 1 configures the
solutions platform to collect data from. In this example, the
measurement tool shows measures of a cognitive measurement score as
a graph plot as compared to data from the individual performance
measures (1, 2, 3, and 4) measured by the measurement tool.
[0193] In the non-limiting example of FIG. 11D, performance measure
1 can be a targeting score, performance measure 2 can be a
navigation score, and performance measure 3 can be a reaction time,
and performance measure 4 can be an interference cost. The
graphical user interface is configured to render one or more
field(s) to display one or more values corresponding to each of the
performance measures, based on the of data collected from the
patient's interactions with a cognitive platform, such as but not
limited to the examples described in connection with any of FIGS.
13-15H hereinbelow. The graphical user interface is configured to
render one or more field(s) to display one or more values computed
based on other performance measures. For example, performance
measure 4 is value of a performance metric such as an interference
cost computed based on the measured values of one or more of
performance measure 1, performance measure 2, and/or performance
measure 3.
[0194] FIGS. 12A-12B show non-limiting examples of the graphical
user interfaces that the solutions platform can be configured to be
rendered to allow user 1, user type 2, and/or user type 3, as
applicable, to enter quantifiers of behavior measures (FIG. 12A) or
symptom measures (FIG. 12B). FIGS. 12A-12B also show non-limiting
examples of the types of measurement fields (1202, 1204, 1206,
1222, 1224, 1226) that can be rendered for display at the graphical
user interface for entry of the ratings and scales by user 1, user
type 2, and/or user type 3. FIGS. 12A-12B also show non-limiting
examples of the types of rating and quantification scales that can
be provided in the measurement fields, such as but not limited to
emoji-based rating scales, text-based scales, or numerical
quantifier rating scales.
[0195] In any example herein, identifying data and information for
any of user 1, user type 2, and/or user type 3, can be
de-identified prior to use, analysis, and/or transmission. In an
example, de-identification can be accomplished by clearing text and
any other identifier from the profile, and assigning a user id that
is not generated using the identifying information.
[0196] In any example herein, identifying data and information for
any of user 1, user type 2, and/or user type 3, can be encrypted
prior to use, analysis, and/or transmission.
[0197] In any example herein, the behavior measure quantifies one
or more behavioral parameters for an individual.
[0198] In any example herein, the symptom measure quantifies one or
more symptoms of a condition of an individual.
[0199] In any example herein, the first plurality of measurement
fields comprises two or more of a behavior measure, a symptom
measure, a medication type designation, a medication compliance
quantifier, and a compliance measure.
[0200] In any example herein, the at least one server configures
the second access means based on at least one control signal from
the first user device.
[0201] In any example herein, the at least one server configures
the third access means based on at least one control signal from
the first user device.
[0202] In any example herein, the at least one processing unit can
be used to receive data indicative of a cognitive measure of an
interaction of an individual with a cognitive tool.
[0203] In any example herein, the server can be used to cause
instructions to be sent to the second user device to display the
second plurality of measurement fields.
[0204] In any example herein, the first access means and/or the
second access means can be a based on a secure link or a secure
push notification.
[0205] In any example herein, the individual can be a child
(including a minor child).
[0206] In any example herein, the collecting first data from the
first user comprises causing the first user device to render a
first graphical user interface, the first graphical user interface
displaying a first plurality of fields, each field of the first
plurality of fields being associated with a first set of behaviors
associated with at least one symptom of a cognitive condition.
[0207] In any example herein, the generating of a reimbursement
report and/or a billing report can be based on the data received
from the first user and/or the third user.
[0208] In any example herein, the adjustments to the type of tasks
and/or CSIs can be made in real-time.
[0209] In any example herein, the cognitive platform and systems
including the cognitive platform can be configured to present
computerized tasks and platform interactions that inform cognitive
assessment (including screening and/or monitoring) or to deliver
cognitive treatment.
[0210] The exemplary cognitive platforms according to the
principles described herein can be applicable to many different
types of neuropsychological conditions, such as but not limited to
dementia, Parkinson's disease, cerebral amyloid angiopathy,
familial amyloid neuropathy, Huntington's disease, or other
neurodegenerative condition, autism spectrum disorder (ASD),
presence of the 16p11.2 duplication, and/or an executive function
disorder (such as but not limited to attention deficit
hyperactivity disorder (ADHD), sensory-processing disorder (SPD),
mild cognitive impairment (MCI), Alzheimer's disease,
multiple-sclerosis, schizophrenia, depression, or anxiety).
[0211] The exemplary cognitive platforms according to the
principles described herein can be applicable to many different
types of neuropsychological conditions, such as but not limited to,
Alzheimer's disease, dementia, Parkinson's disease, cerebral
amyloid angiopathy, familial amyloid neuropathy, or Huntington's
disease.
[0212] Any classification of an individual as to likelihood of
onset and/or stage of progression of a condition (including a
neurodegenerative condition) according to the principles herein can
be transmitted as part of an enhanced analysis report as a signal
to a medical device, healthcare computing system, or other device,
and/or to a medical practitioner, a health practitioner, a physical
therapist, a behavioral therapist, a sports medicine practitioner,
a pharmacist, or other practitioner, to allow formulation of a
course of treatment for the individual or to modify an existing
course of treatment, including to determine a change in dosage of a
drug, biologic or other pharmaceutical agent to the individual or
to determine an optimal type or combination of drug, biologic or
other pharmaceutical agent to the individual.
[0213] In any example herein, the cognitive platform can be
configured as any combination of a medical device platform, a
monitoring device platform, a screening device platform, or other
device platform.
[0214] In non-limiting examples, the measurement tool data can be
collected from measurements using one or more physiological or
monitoring components and/or cognitive testing components. In any
example herein, the one or more physiological components are
configured for performing physiological measurements. The
physiological measurements provide quantitative measurement data of
physiological parameters and/or data that can be used for
visualization of physiological structure and/or functions.
[0215] It is understood that reference to "drug" herein encompasses
a drug, a biologic and/or other pharmaceutical agent.
[0216] In a non-limiting example, the physiological instrument can
be a fMRI, and the data can be measurement data indicative of the
cortical thickness, brain functional activity changes, or other
measure.
[0217] In other non-limiting examples, measurement tool data can
include any data that can be used to characterize an individual's
status, such as but not limited to age, gender or other similar
data.
[0218] In any example herein, the data (including the data from the
measurement fields, identifying data, and/or data from the
measurement tool(s)) is collected with the individual's
consent.
[0219] In any example herein, an individual consults with a
healthcare practitioner prior to making any changes to a drug or
other medication being taken, or to a regimen set for taking the
drug or other medication.
[0220] In any example herein, the one or more physiological
components can include any means of measuring physical
characteristics of the body and nervous system, including
electrical activity, heart rate, blood flow, and oxygenation
levels, to provide the measurement tool data. This can include
camera-based heart rate detection, measurement of galvanic skin
response, blood pressure measurement, electroencephalogram,
electrocardiogram, magnetic resonance imaging, near-infrared
spectroscopy, and/or pupil dilation measures, to provide the
measurement tool data.
[0221] Other examples of physiological measurements to provide
measurement tool data include, but are not limited to, the
measurement of body temperature, heart or other cardiac-related
functioning using an electrocardiograph (ECG), electrical activity
using an electroencephalogram (EEG), event-related potentials
(ERPs), functional magnetic resonance imaging (fMRI), blood
pressure, electrical potential at a portion of the skin, galvanic
skin response (GSR), magneto-encephalogram (MEG), eye-tracking
device or other optical detection device including processing units
programmed to determine degree of pupillary dilation, functional
near-infrared spectroscopy (fNIRS), and/or a positron emission
tomography (PET) scanner. An EEG-fMRI or MEG-fMRI measurement
allows for simultaneous acquisition of electrophysiology (EEG/MEG)
data and hemodynamic (fMRI) data.
Non-Limiting Exemplary Cognitive Platforms and Platform
Products
[0222] In any example herein, the cognitive platform can be
configured for cognitive monitoring, cognitive assessment,
cognitive screening, and/or cognitive treatment. Data derived from
the cognitive platform can include one or more performance metrics
and/or data indicative of cognitive abilities of the individual,
generated based on the individual's interactions with the cognitive
platform.
[0223] The exemplary cognitive platform can be configured for
measuring data indicative of a user's performance at one or more
tasks, to provide a user performance metric. The exemplary tasks
may include an interference processing task, and/or a spatial
navigation task, and/or an emotional/affective task. The exemplary
performance metric can be used to derive an assessment of a user's
cognitive abilities and/or to measure a user's response to a
cognitive treatment, and/or to provide data or other quantitative
indicia of a user's condition (including physiological condition
and/or cognitive condition). Non-limiting exemplary cognitive
platforms or platform products according to the principles herein
can be configured to classify an individual as to an condition, the
expression level of protein(s) that can be of clinical interest in
the condition, and/or potential efficacy of use of the cognitive
platform and/or platform product when the individual is
administered a drug, biologic or other pharmaceutical agent, based
on the data collected from the individual's interaction with the
cognitive platform and/or platform product and/or metrics computed
based on the analysis (and associated computations) of that data.
Yet other non-limiting exemplary cognitive platforms or platform
products according to the principles herein can be configured to
classify an individual as to likelihood of onset and/or stage of
progression of the condition, based on the data collected from the
individual's interaction with the cognitive platform and/or
platform product and/or metrics computed based on the analysis (and
associated computations) of that data.
[0224] Any classification of an individual as to likelihood of
onset and/or stage of progression of the condition according to the
principles herein can be transmitted as a signal to a medical
device, healthcare computing system, or other device, and/or to a
medical practitioner, a health practitioner, a physical therapist,
a behavioral therapist, a sports medicine practitioner, a
pharmacist, or other practitioner, to allow formulation of a course
of treatment for the individual or to modify an existing course of
treatment, including to determine a change in dosage of a drug,
biologic or other pharmaceutical agent to the individual or to
determine an optimal type or combination of drug, biologic or other
pharmaceutical agent to the individual.
[0225] In any example herein, the platform product or cognitive
platform can be configured as any combination of a medical device
platform, a monitoring device platform, a screening device
platform, or other device platform.
[0226] The instant disclosure is also directed to exemplary systems
that include platform products and cognitive platforms that are
configured for coupling with one or more physiological or
monitoring component and/or cognitive testing component. In some
examples, the systems include platform products and cognitive
platforms that are integrated with the one or more other
physiological or monitoring component and/or cognitive testing
component. In other examples, the systems include platform products
and cognitive platforms that are separately housed from and
configured for communicating with the one or more physiological or
monitoring component and/or cognitive testing component, to receive
data indicative of measurements made using such one or more
components.
[0227] As used herein, the term "cData" refers to data collected
from measures of an interaction of a user with a
computer-implemented device formed as a platform product or a
cognitive platform.
[0228] As used herein, the term "nData" refers to other types of
data that can be collected according to the principles herein. Any
component used to provide nData is referred to herein as a nData
component.
[0229] In any example herein, the cData and/or nData can be
collected in real-time.
[0230] In non-limiting examples, the nData can be collected from
measurements using one or more physiological or monitoring
components and/or cognitive testing components. In any example
herein, the one or more physiological components are configured for
performing physiological measurements. The physiological
measurements provide quantitative measurement data of physiological
parameters and/or data that can be used for visualization of
physiological structure and/or functions.
[0231] As a non-limiting example, nData can be collected from
measurements of types of protein and/or conformation of proteins in
the tissue or fluid (including blood) of an individual and/or in
tissue or fluid (including blood) collected from the individual. In
some examples, the tissue and or fluid can be in or taken from the
individual's brain. In other examples, the measurement of the
conformation of the proteins can provide an indication of protein
formation (e.g., whether the proteins are forming aggregates). The
expression group can be defined based on a threshold expression
level of the protein of clinical interest in the neurodegenerative
condition, where a measured value of expression level above a
pre-specified threshold defines a first expression group and a
measured value of expression level below the pre-specified
threshold defines a second expression group.
[0232] It is understood that reference to "drug" herein encompasses
a drug, a biologic and/or other pharmaceutical agent.
[0233] In a non-limiting example, the physiological instrument can
be a fMRI, and the nData can be measurement data indicative of the
cortical thickness, brain functional activity changes, or other
measure.
[0234] In other non-limiting examples, nData can include any data
that can be used to characterize an individual's status, such as
but not limited to age, gender or other similar data.
[0235] In any example herein, the data (including cData and nData)
is collected with the individual's consent.
[0236] In any example herein, the one or more physiological
components can include any means of measuring physical
characteristics of the body and nervous system, including
electrical activity, heart rate, blood flow, and oxygenation
levels, to provide the nData. This can include camera-based heart
rate detection, measurement of galvanic skin response, blood
pressure measurement, electroencephalogram, electrocardiogram,
magnetic resonance imaging, near-infrared spectroscopy, and/or
pupil dilation measures, to provide the nData.
[0237] Other examples of physiological measurements to provide
nData include, but are not limited to, the measurement of body
temperature, heart or other cardiac-related functioning using an
electrocardiograph (ECG), electrical activity using an
electroencephalogram (EEG), event-related potentials (ERPs),
functional magnetic resonance imaging (fMRI), blood pressure,
electrical potential at a portion of the skin, galvanic skin
response (GSR), magneto-encephalogram (MEG), eye-tracking device or
other optical detection device including processing units
programmed to determine degree of pupillary dilation, functional
near-infrared spectroscopy (fNIRS), and/or a positron emission
tomography (PET) scanner. An EEG-fMRI or MEG-fMRI measurement
allows for simultaneous acquisition of electrophysiology (EEG/MEG)
nData and hemodynamic (fMRI) nData.
[0238] The fMRI also can be used to provide provides measurement
data (nData) indicative of neuronal activation, based on the
difference in magnetic properties of oxygenated versus
de-oxygenated blood supply to the brain. The fMRI can provide an
indirect measure of neuronal activity by measuring regional changes
in blood supply, based on a positive correlation between neuronal
activity and brain metabolism.
[0239] A PET scanner can be used to perform functional imaging to
observe metabolic processes and other physiological measures of the
body through detection of gamma rays emitted indirectly by a
positron-emitting radionuclide (a tracer). The tracer can be
introduced into the user's body using a biologically-active
molecule. Indicators of the metabolic processes and other
physiological measures of the body can be derived from the scans,
including from computer reconstruction of two- and
three-dimensional images of from nData of tracer concentration from
the scans. The nData can include measures of the tracer
concentration and/or the PET images (such as two- or
three-dimensional images).
[0240] In any example herein, the task can be a spatial navigation
task according to the principles herein. In this example, a
computing device is configured to render a view of a landscape,
such as but not limited to the example of FIG. 13. FIG. 13 shows an
elevated, overhead view of a landscape 1310 that includes one or
more internal course 1312 and obstacles 1314. In this example,
portions of the course 1312 are configured to include pathways and
passageways that allow traversal of an avatar or other guidable
element 1316. The navigation task requires an individual to
formulate a pathway about the strategically positioned obstacles
1314 from an initial point ("A") to at least one target location
("B"). The computing device can be configured to present
instructions to the individual to navigate the course 1312. The
computing device also can be configured to provide an individual
with an input device or other type of control element that allows
the individual to traverse the course 1312, including specifying
and/or controlling one or more of the speed of movement,
orientation, velocity, choice of navigation strategy, the wait or
delay period, or other period of inaction, prior to continuing in a
given direction of a course or changing direction, time interval to
complete a course, and/or frequency or number of times of referral
to an aerial or elevated view of a landscape (including as a map),
including values of any of these parameters as a function of time.
As another non-limiting example, the performance metrics can
include a measure of the degree of optimization of the path
navigated by the individual through the course, such as though
determining the shortest path or near-shortest path through the
course.
[0241] The computing device can be configured to collect data
indicative of the performance metric that quantifies the navigation
strategy employed by the individual from the initial point ("A") to
reach one or more target points ("B"). For example, the computing
device can be configured to collect data indicative of the
individual's decision to proceed from the initial point ("A") along
the dashed line or the dotted line, the speed of movement, the
orientation of the avatar or other guidable element, among other
measures. In the various examples, performance metrics that can be
measured using the computing device can include data indicative of
the speed of movement, orientation, velocity, choice of navigation
strategy, wait or delay period, or other period of inaction, prior
to continuing in a given direction of a course or changing
direction, time interval to complete a course, and/or frequency or
number of times of referral to an aerial or elevated view of a
landscape (including as a map), including values of any of these
parameters as a function of time. As another non-limiting example,
the performance metrics can include a measure of the degree of
optimization of the path navigated by the individual through the
course, such as though determining the shortest path or
near-shortest path through the course.
[0242] In another example herein, a task can involve one or more
activities that a user is required to engage in. Any one or more of
the tasks can be computer-implemented as computerized stimuli or
interaction (described in greater detail below).
[0243] For a targeting task, the cognitive platform may require
temporally-specific and/or position-specific responses from a user.
For a navigation task, the cognitive platform may require
position-specific and/or motion-specific responses from the user.
For a facial expression recognition or object recognition task, the
cognitive platform may require temporally-specific and/or
position-specific responses from the user. The multi-tasking tasks
can include any combination of two or more tasks. In non-limiting
examples, the user response to tasks, such as but not limited to
targeting and/or navigation and/or facial expression recognition or
object recognition task(s), can be recorded using an input device
of the cognitive platform. Non-limiting examples of such input
devices can include a touch, swipe or other gesture relative to a
user interface or image capture device (such as but not limited to
a touch-screen or other pressure sensitive screen, or a camera),
including any form of graphical user interface configured for
recording a user interaction. In other non-limiting examples, the
user response recorded using the cognitive platform for tasks, such
as but not limited to targeting and/or navigation and/or facial
expression recognition or object recognition task(s), can include
user actions that cause changes in a position, orientation, or
movement of a computing device including the cognitive platform.
Such changes in a position, orientation, or movement of a computing
device can be recorded using an input device disposed in or
otherwise coupled to the computing device, such as but not limited
to a sensor. Non-limiting examples of sensors include a motion
sensor, position sensor, and/or an image capture device (such as
but not limited to a camera).
[0244] FIGS. 14A-15H show non-limiting exemplary user interfaces
that can be rendered using exemplary systems, methods, and
apparatus herein to render the tasks and/or interferences (either
or both with computerized element) for user interactions. The
non-limiting exemplary user interfaces of FIGS. 14A-5H also can be
used for one or more of: to collect the data indicative of the
individual's responses to the tasks and/or the interferences and
the computerized element, to show progress metrics, or to provide
the analysis metrics.
[0245] FIGS. 14A-14D show exemplary of the features of object(s)
(targets or non-targets) that can be rendered as time-varying
characteristics to an exemplary user interface, according to the
principles herein. FIG. 14A shows an example where the modification
to the time-varying characteristics of an aspect of the object 1400
rendered to the user interface is a dynamic change in position
and/or speed of the object 1400 relative to environment rendered in
the graphical user interface. FIG. 14B shows an example where the
modification to the time-varying characteristics of an aspect of
the object 1402 rendered to the user interface is a dynamic change
in size and/or direction of trajectory/motion, and/or orientation
of the object 1402 relative to the environment rendered in the
graphical user interface. FIG. 14C shows an example where the
modification to the time-varying characteristics of an aspect of
the object 1404 rendered to the user interface is a dynamic change
in shape or other type of the object 1404 relative to the
environment rendered in the graphical user interface. In this
non-limiting example, the time-varying characteristic of object
1404 is effected using morphing from a first type of object (a star
object) to a second type of object (a round object). In another
non-limiting example, the time-varying characteristic of object
1404 is effected by rendering a blendshape as a proportionate
combination of a first type of object and a second type of object.
FIG. 14C shows an example in which the modification to the
time-varying characteristics of an aspect of the object 1404
rendered to the user interface is a dynamic change in shape or
other type of the object 1404 rendered in the graphical user
interface (in this non-limiting example, from a star object to a
round object). FIG. 14D shows an example where the modification to
the time-varying characteristics of an aspect of the object 1406
rendered to the user interface is a dynamic change in pattern, or
color, or visual feature of the object 1406 relative to environment
rendered in the graphical user interface (in this non-limiting
example, from a star object having a first pattern to a round
object having a second pattern). In another non-limiting example,
the time-varying characteristic of object can be a rate of change
of a facial expression depicted on or relative to the object. In
any example herein, the foregoing time-varying characteristic can
be applied to an object including the computerized element to
modify an cognitive or emotional load of the individual's
interaction with the apparatus (e.g., computing device or cognitive
platform).
[0246] FIGS. 15A-15H show a non-limiting example of the dynamics of
tasks and interferences that can be rendered at user interfaces,
according to the principles herein. In this example, the primary
task is a visuo-motor navigation task, and the interference is
target discrimination (as a secondary task). As shown in FIGS. 15D,
the individual is required to perform the navigation task by
controlling the motion of the avatar 1502 along a path that
coincides with the milestone objects 1504. FIGS. 15A-15H show a
non-limiting exemplary implementation where the individual is
expected to actuate an apparatus or computing device (or other
sensing device) to cause the avatar 1502 to coincide with the
milestone object 1504 as the response in the navigation task, with
scoring based on the success of the individual at crossing paths
with (e.g., hitting) the milestone objects 1504. In another
example, the individual is expected to actuate an apparatus or
computing device (or other sensing device) to cause the avatar 1502
to miss the milestone object 1504, with scoring based on the
success of the individual at avoiding the milestone objects 1504.
FIGS. 15A-15C show the dynamics of a target object 1506 (a star
having a first type of pattern). FIGS. 15E-15H show the dynamics of
a non-target object 1508 (a star having a second type of
pattern).
[0247] In the example of FIGS. 15A-15H, the processing unit of the
exemplary system, method, and apparatus is configured to receive
data indicative of the individual's physical actions to cause the
avatar 1502 to navigate the path. For example, the individual may
be required to perform physical actions to "steer" the avatar,
e.g., by changing the rotational orientation or otherwise moving a
computing device. Such action can cause a gyroscope or
accelerometer or other motion or position sensor device to detect
the movement, thereby providing measurement data indicative of the
individual's degree of success in performing the navigation
task.
[0248] In the example of FIGS. 15A-15C and 15E-15H, the processing
unit of the exemplary system, method, and apparatus is configured
to receive data indicative of the individual's physical actions to
perform the target discrimination task. For example, the individual
may be instructed prior to a trial or other session to tap, or make
other physical indication, in response to display of a target
object 1506, and not to tap to make the physical indication in
response to display of a non-target object 1508. In FIGS. 15A-15C
and 15E-15H, the target discrimination task acts as an interference
(i.e., an instance of a secondary task) to the primary navigation
task, in an interference processing multi-tasking implementation.
As described hereinabove, the exemplary systems, methods, and
apparatus can cause the processing unit to render a display feature
to display the instructions to the individual as to the expected
performance. As also described hereinabove, the processing unit of
the exemplary system, method, and apparatus can be configured to
(i) receive the data indicative of the measure of the degree and
type of the individual's response to the primary task substantially
simultaneously as the data indicative of the measure of the degree
and type of the individual's response to the interference is
collected (whether the interference includes a target or a
non-target), or (ii) to selectively receive data indicative of the
measure of the degree and type of the individual's response to an
interference that includes a target stimulus (i.e., an interruptor)
substantially simultaneously (i.e., at substantially the same time)
as the data indicative of the measure of the degree and type of the
individual's response to the task is collected and to selectively
not collect the measure of the degree and type of the individual's
response to an interference that includes a non-target stimulus
(i.e., a distraction) substantially simultaneously (i.e., at
substantially the same time) as the data indicative of the measure
of the degree and type of the individual's response to the task is
collected.
[0249] In an exemplary implementation involving multi-tasking
tasks, the computer device is configured (such as using at least
one specially-programmed processing unit) to cause the cognitive
platform to present to a user two or more different type of tasks,
such as but not limited to, targeting and/or navigation and/or
facial expression recognition or object recognition tasks, during a
short time frame (including in real-time and/or substantially
simultaneously). The computer device is also configured (such as
using at least one specially-programmed processing unit) to collect
data indicative of the type of user response received to the
multi-tasking tasks, within the short time frame (including in
real-time and/or substantially simultaneously). In these examples,
the two or more different types of tasks can be presented to the
individual within the short time frame (including in real-time
and/or substantially simultaneously), and the computing device can
be configured to receive data indicative of the user response(s)
relative to the two or more different types of tasks within the
short time frame (including in real-time and/or substantially
simultaneously).
[0250] In some examples, the short time frame can be of any time
interval at a resolution of up to about 1.0 millisecond or greater.
The time intervals can be, but are not limited to, durations of
time of any division of a periodicity of about 2.0 milliseconds or
greater, up to any reasonable end time. The time intervals can be,
but are not limited to, about 3.0 millisecond, about 5.0
millisecond, about 10 milliseconds, about 25 milliseconds, about 40
milliseconds, about 50 milliseconds, about 60 milliseconds, about
70 milliseconds, about 100 milliseconds, or greater. In other
examples, the short time frame can be, but is not limited to,
fractions of a second, about a second, between about 1.0 and about
2.0 seconds, or up to about 2.0 seconds, or more.
[0251] In some examples, the platform product or cognitive platform
can be configured to collect data indicative of a reaction time of
a user's response relative to the time of presentation of the
tasks. For example, the computing device can be configured to cause
the platform product or cognitive platform to provide smaller or
larger reaction time window for a user to provide a response to the
tasks as a way of adjusting the difficulty level.
[0252] As used herein, the term "computerized stimuli or
interaction" or "CSI" refers to a computerized element that is
presented to a user to facilitate the user's interaction with a
stimulus or other interaction. As non-limiting examples, the
computing device can be configured to present auditory stimulus or
initiate other auditory-based interaction with the user, and/or to
present vibrational stimuli or initiate other vibrational-based
interaction with the user, and/or to present tactile stimuli or
initiate other tactile-based interaction with the user, and/or to
present visual stimuli or initiate other visual-based interaction
with the user.
[0253] Any task according to the principles herein can be presented
to a user via a computing device, actuating component, or other
device that is used to implement one or more stimuli or other
interactive element. For example, the task can be presented to a
user by rendering a graphical user interface to present the
computerized stimuli or interaction (CSI) or other interactive
elements. In other examples, the task can be presented to a user as
auditory, tactile, or vibrational computerized elements (including
CSIs) using an actuating component. Description of use of (and
analysis of data from) one or more CSIs in the various examples
herein also encompasses use of (and analysis of data from) tasks
comprising the one or more CSIs in those examples.
[0254] In an example where the computing device is configured to
present visual CSI, the CSI can be rendered using at least one
graphical user interface to be presented to a user. In some
examples, at least one graphical user interface is configured for
measuring responses as the user interacts with CSI computerized
element rendered using the at least one graphical user interface.
In a non-limiting example, the graphical user interface can be
configured such that the CSI computerized element(s) are active,
and may require at least one response from a user, such that the
graphical user interface is configured to measure data indicative
of the type or degree of interaction of the user with the platform
product. In another example, the graphical user interface can be
configured such that the CSI computerized element(s) are a passive
and are presented to the user using the at least one graphical user
interface but may not require a response from the user. In this
example, the at least one graphical user interface can be
configured to exclude the recorded response of an interaction of
the user, to apply a weighting factor to the data indicative of the
response (e.g., to weight the response to lower or higher values),
or to measure data indicative of the response of the user with the
platform product as a measure of a misdirected response of the user
(e.g., to issue a notification or other feedback to the user of the
misdirected response).
[0255] In an example, the cognitive platform and/or platform
product can be configured as a processor-implemented system, method
or apparatus that includes and at least one processing unit. In an
example, the at least one processing unit can be programmed to
render at least one graphical user interface to present the
computerized stimuli or interaction (CSI) or other interactive
elements to the user for interaction. In other examples, the at
least one processing unit can be programmed to cause an actuating
component of the platform product to effect auditory, tactile, or
vibrational computerized elements (including CSIs) to effect the
stimulus or other interaction with the user. The at least one
processing unit can be programmed to cause a component of the
program product to receive data indicative of at least one user
response based on the user interaction with the CSI or other
interactive element (such as but not limited to cData), including
responses provided using the input device. In an example where at
least one graphical user interface is rendered to present the
computerized stimuli or interaction (CSI) or other interactive
elements to the user, the at least one processing unit can be
programmed to cause graphical user interface to receive the data
indicative of at least one user response. The at least one
processing unit also can be programmed to: analyze the cData to
provide a measure of the individual's cognitive condition, and/or
analyze the differences in the individual's performance based on
determining the differences between the user's responses (including
based on differences in the cData), and/or adjust the difficulty
level of the auditory, tactile, or vibrational computerized
elements (including CSIs), the CSIs or other interactive elements
based on the analysis of the cData (including the measures of the
individual's performance determined in the analysis), and/or
provide an output or other feedback from the platform product that
can be indicative of the individual's performance, and/or cognitive
assessment, and/or response to cognitive treatment, and/or assessed
measures of cognition. In non-limiting examples, the at least one
processing unit also can be programmed to classify an individual as
to an condition, the expression level of protein(s) that can be of
clinical interest in the condition, and/or potential efficacy of
use of the cognitive platform and/or platform product when the
individual is administered a drug, biologic or other pharmaceutical
agent, based on the cData collected from the individual's
interaction with the cognitive platform and/or platform product
and/or metrics computed based on the analysis (and associated
computations) of that cData. In non-limiting examples, the at least
one processing unit also can be programmed to classify an
individual as to likelihood of onset and/or stage of progression of
an condition, based on the cData collected from the individual's
interaction with the cognitive platform and/or platform product
and/or metrics computed based on the analysis (and associated
computations) of that cData. The neurodegenerative condition can
be, but is not limited to, lupus or multiple sclerosis.
[0256] An exemplary system, method, and apparatus according to the
principles herein includes a platform product (including using an
APP) that uses a cognitive platform configured to render at least
one emotional/affective element (EAE), to add emotional processing
as an overt component for tasks in MTG or STG. In one example, the
EAE is used in the tasks configured to assess cognition or improve
cognition related to emotions, and the data (including cData)
collected as a measure of user interaction with the rendered EAE in
the platform product is used to determine the measures of the
assessment of cognition or the improvement to measures of cognition
after a treatment configured for interaction using the graphical
user interface, or as auditory, tactile, or vibrational elements,
of the platform product. The EAE can be configured to collect data
to measure the impact of emotions on non-emotional cognition, such
as by causing the graphical user interface to render spatial tasks
for the user to perform under emotional load, and/or to collect
data to measure the impact of non-emotional cognition on emotions,
such as by causing the graphical user interface to render features
that employ measures of executive function to regulate emotions. In
one exemplary implementation, the graphical user interface can be
configured to render tasks for identifying the emotion indicated by
the CSI (based on measurement data), maintaining that
identification in working memory, and comparing it with the
measures of emotion indicated by subsequent CSI, while under
cognitive load due to MTG.
[0257] In other examples, the platform product can be configured as
a processor-implemented system, method or apparatus that includes a
display component, an input device, and the at least one processing
unit. The at least one processing unit can be programmed to render
at least one graphical user interface, for display at the display
component, to present the computerized stimuli or interaction (CSI)
or other interactive elements to the user for interaction. In other
examples, the at least one processing unit can be programmed to
cause an actuating component of the platform product to effect
auditory, tactile, or vibrational computerized elements (including
CSIs) to effect the stimulus or other interaction with the
user.
[0258] Non-limiting examples of an input device include a
touch-screen, or other pressure-sensitive or touch-sensitive
surface, a motion sensor, a position sensor, a pressure sensor,
joystick, exercise equipment, and/or an image capture device (such
as but not limited to a camera).
[0259] In any example, the input device is configured to include at
least one component configured to receive input data indicative of
a physical action of the individual(s), where the data provides a
measure of the physical action of the individual(s) in interacting
with the cognitive platform and/or platform product, e.g., to
perform the one or more tasks and/or tasks with interference.
[0260] The analysis of the individual's performance may include
using the computing device to compute percent accuracy, number of
hits and/or misses during a session or from a previously completed
session. Other indicia that can be used to compute performance
measures is the amount time the individual takes to respond after
the presentation of a task (e.g., as a targeting stimulus). Other
indicia can include, but are not limited to, reaction time,
response variance, number of correct hits, omission errors, false
alarms, learning rate, spatial deviance, subjective ratings, and/or
performance threshold, etc.
[0261] In a non-limiting example, the user's performance can be
further analyzed to compare the effects of two different types of
tasks on the user's performances, where these tasks present
different types of interferences (e.g., a distraction or an
interruptor). The computing device is configured to present the
different types of interference as CSIs or other interactive
elements that divert the user's attention from a primary task. For
a distraction, the computing device is configured to instruct the
individual to provide a primary response to the primary task and
not to provide a response (i.e., to ignore the distraction). For an
interruptor, the computing device is configured to instruct the
individual to provide a response as a secondary task, and the
computing device is configured to obtain data indicative of the
user's secondary response to the interruptor within a short time
frame (including at substantially the same time) as the user's
response to the primary task (where the response is collected using
at least one input device). The computing device is configured to
compute measures of one or more of a user's performance at the
primary task without an interference, performance with the
interference being a distraction, and performance with the
interference being an interruption. The user's performance metrics
can be computed based on these measures. For example, the user's
performance can be computed as a cost (performance change) for each
type of interference (e.g., distraction cost and
interruptor/multi-tasking cost). The user's performance level on
the tasks can be analyzed and reported as feedback, including
either as feedback to the cognitive platform for use to adjust the
difficulty level of the tasks, and/or as feedback to the individual
concerning the user's status or progression.
[0262] In a non-limiting example, the computing device can also be
configured to analyze, store, and/or output the reaction time for
the user's response and/or any statistical measures for the
individual's performance (e.g., percentage of correct or incorrect
response in the last number of sessions, over a specified duration
of time, or specific for a type of tasks (including non-target
and/or target stimuli, a specific type of task, etc.).
[0263] In a non-limiting example, the computerized element includes
at least one task rendered at a graphical user interface as a
visual task or presented as an auditory, tactile, or vibrational
task. Each task can be rendered as interactive mechanics that are
designed to elicit a response from a user after the user is exposed
to stimuli for the purpose of cData and/or nData collection.
[0264] In a non-limiting example, the computerized element includes
at least one platform interaction (gameplay) element of the
platform rendered at a graphical user interface, or as auditory,
tactile, or vibrational element of a program product. Each platform
interaction (gameplay) element of the platform product can include
interactive mechanics (including in the form of videogame-like
mechanics) or visual (or cosmetic) features that may or may not be
targets for cData and/or nData collection.
[0265] As used herein, the term "gameplay" encompasses a user
interaction (including other user experience) with aspects of the
platform product.
[0266] In a non-limiting example, the computerized element includes
at least one element to indicate positive feedback to a user. Each
element can include an auditory signal and/or a visual signal
emitted to the user that indicates success at a task or other
platform interaction element, i.e., that the user responses at the
platform product has exceeded a threshold success measure on a task
or platform interaction (gameplay) element.
[0267] In a non-limiting example, the computerized element includes
at least one element to indicate negative feedback to a user. Each
element can include an auditory signal and/or a visual signal
emitted to the user that indicates failure at a task or platform
interaction (gameplay) element, i.e., that the user responses at
the platform product has not met a threshold success measure on a
task or platform interaction element.
[0268] In a non-limiting example, the computerized element includes
at least one element for messaging, i.e., a communication to the
user that is different from positive feedback or negative
feedback.
[0269] In a non-limiting example, the computerized element includes
at least one element for indicating a reward. A reward computer
element can be a computer generated feature that is delivered to a
user to promote user satisfaction with the CSIs and as a result,
increase positive user interaction (and hence enjoyment of the user
experience).
[0270] In a non-limiting example, the cognitive platform can be
configured to render multi-task interactive elements. In some
examples, the multi-task interactive elements are referred to as
multi-task gameplay (MTG). The multi-task interactive elements
include interactive mechanics configured to engage the user in
multiple temporally-overlapping tasks, i.e., tasks that may require
multiple, substantially simultaneous responses from a user.
[0271] In a non-limiting example, the cognitive platform can be
configured to render single-task interactive elements. In some
examples, the single-task interactive elements are referred to as
single-task gameplay (STG). The single-task interactive elements
include interactive mechanics configured to engage the user in a
single task in a given time interval.
[0272] According to the principles herein, the term "cognition" or
"cognitive" refers to the mental action or process of acquiring
knowledge and understanding through thought, experience, and the
senses. This includes, but is not limited to, psychological
concepts/domains such as, executive function, memory, perception,
attention, emotion, motor control, and interference processing. An
exemplary computer-implemented device according to the principles
herein can be configured to collect data indicative of user
interaction with a platform product, and to compute metrics that
quantify user performance. The quantifiers of user performance can
be used to provide measures of cognition (for cognitive assessment)
or to provide measures of status or progress of a cognitive
treatment.
[0273] According to the principles herein, the term "treatment" or
"treat" refers to any manipulation of CSI in a platform product
(including in the form of an APP) that results in a measurable
improvement of the abilities of a user, such as but not limited to
improvements related to cognition, a user's mood, emotional state,
and/or level of engagement or attention to the cognitive platform.
The degree or level of improvement can be quantified based on user
performance measures as describe herein. In an example, the term
"treatment" may also refer to a therapy.
[0274] According to the principles herein, the term "session"
refers to a discrete time period, with a clear start and finish,
during which a user interacts with a platform product to receive
assessment or treatment from the platform product (including in the
form of an APP).
[0275] According to the principles herein, the term "assessment"
refers to at least one session of user interaction with CSIs or
other feature or element of a platform product. The data collected
from one or more assessments performed by a user using a platform
product (including in the form of an APP) can be used as to derive
measures or other quantifiers of cognition, or other aspects of a
user's abilities.
[0276] According to the principles herein, the term "emotional
load" refers to cognitive load that is specifically associated with
processing emotional information or regulating emotions.
[0277] According to the principles herein, the term "cognitive
load" refers to the amount of mental resources that a user may need
to expend to complete a task. This term also can be used to refer
to the challenge or difficulty level of a task or gameplay.
[0278] In an example, the platform product comprises a computing
device that is configured to present to a user a cognitive platform
based on interference processing. In an exemplary system, method
and apparatus that implements interference processing, at least one
processing unit is programmed to render at least one first
graphical user interface or cause an actuating component to
generate an auditory, tactile, or vibrational signal, to present
first CSIs as a first task that requires a first type of response
from a user. The exemplary system, method and apparatus is also
configured to cause the at least one processing unit to render at
least one second graphical user interface or cause the actuating
component to generate an auditory, tactile, or vibrational signal,
to present second CSIs as a first interference with the first task,
requiring a second type of response from the user to the first task
in the presence of the first interference. In a non-limiting
example, the second type of response can include the first type of
response to the first task and a secondary response to the first
interference. In another non-limiting example, the second type of
response may not include, and be quite different from, the first
type of response. The at least one processing unit is also
programmed to receive data indicative of the first type of response
and the second type of response based on the user interaction with
the platform product (such as but not limited to cData), such as
but not limited to by rendering the at least one graphical user
interface to receive the data. The platform product also can be
configured to receive nData indicative of measurements made before,
during, and/or after the user interacts with the cognitive platform
(including nData from measurements of physiological or monitoring
components and/or cognitive testing components). The at least one
processing unit also can be programmed to: analyze the cData and/or
nData to provide a measure of the individual's condition (including
physiological and/or cognitive condition), and/or analyze the
differences in the individual's performance based on determining
the differences between the measures of the user's first type and
second type of responses (including based on differences in the
cData) and differences in the associated nData. The at least one
processing unit also can be programmed to: adjust the difficulty
level of the first task and/or the first interference based on the
analysis of the cData and/or nData (including the measures of the
individual's performance and/or condition (including physiological
and/or cognitive condition) determined in the analysis), and/or
provide an output or other feedback from the platform product that
can be indicative of the individual's performance, and/or cognitive
assessment, and/or response to cognitive treatment, and/or assessed
measures of cognition. In non-limiting examples, the at least one
processing unit also can be programmed to classify an individual as
to an condition, the expression level of protein(s) that can be of
clinical interest in the condition, and/or potential efficacy of
use of the cognitive platform and/or platform product when the
individual is administered a drug, biologic or other pharmaceutical
agent, based on nData and the cData collected from the individual's
interaction with the cognitive platform and/or platform product
and/or metrics computed based on the analysis (and associated
computations) of that cData and the nData. In non-limiting
examples, the at least one processing unit also can be programmed
to classify an individual as to likelihood of onset and/or stage of
progression of an condition, based on nData and the cData collected
from the individual's interaction with the cognitive platform
and/or platform product and/or metrics computed based on the
analysis (and associated computations) of that cData and the nData.
The condition can be, but is not limited to, lupus and multiple
sclerosis.
[0279] In an example, the feedback from the differences in the
individual's performance based on determining the differences
between the measures of the user's first type and second type of
responses and the nData can be used as an input in the cognitive
platform that indicates real-time performance of the individual
during one or more session(s). The data of the feedback can be used
to as an input to a computation component of the computing device
to determine a degree of adjustment that the cognitive platform
makes to a difficulty level of the first task and/or the first
interference that the user interacts within the same ongoing
session and/or within a subsequently-performed session.
[0280] As a non-limiting example, the cognitive platform based on
interference processing can be a cognitive platform based on the
Project: EVO.TM. platform by Akili Interactive Labs, Inc. (Boston,
Mass.).
[0281] In an exemplary system, method and apparatus according to
the principles herein that is based on interference processing, the
graphical user interface is configured such that, as a component of
the interference processing, one of the discriminating features of
the targeting task that the user responds to is a feature in the
platform that displays an emotion, a shape, a color, and/or a
position that serves as an interference element in interference
processing.
EXAMPLES
[0282] A health screen with health tips may include the following
features:
TABLE-US-00001 Page Element Functional Requirement Health When a
user tabs on this Tips sub subtab, he or she is shown tab and list
a list of cards - one for of health each relevant external tips
resource article that meets the queries. The cards are refreshed
every 7 days. There is infinite scroll pagination Health tip A
photo is provided via the card - API photo The article's headline
is shown as a link When a user touches the card, he or she is taken
to the relevant article page Health tip The article headline is
card - no shown as a link. photo When a user touches the card, he
or she is taken to the relevant article page Like When a user
touches the button heart, it fills in to give (heart) feedback that
it has been pressed and the overall number increases by 1. When the
user touches a filled heart, the heart unfills and the overall
number decreases by 1
[0283] An article page may include the following features:
TABLE-US-00002 Page Element Functional Requirement Navigation When
a user touches the <- arrow, he or she is taken to a reference
screen. Section title Health Tips Article Title Shows the title of
the article Like button When a user touches the heart, it fills
(heart) in to give feedback that it has been pressed and the
overall number increases by 1. When the user touches a filled
heart, the heart unfills and the overall number decreases by 1
Image/photo If a photo/image is available, show it on the article
page. If not, do not show it on the article page. Article copy Show
the article copy on the page Related Show 1-3 health tips that
match the health tips behavior variable of the current article If
no related health tips, do not show the subheading
[0284] Conclusion
[0285] The above-described embodiments can be implemented in any of
numerous ways. For example, some embodiments may be implemented
using hardware, software or a combination thereof. When any aspect
of an embodiment is implemented at least in part in software, the
software code can be executed on any suitable processor or
collection of processors, whether provided in a single computer or
distributed among multiple computers.
[0286] In this respect, various aspects of the disclosure may be
embodied at least in part as a computer readable storage medium (or
multiple computer readable storage media) (e.g., a computer memory,
compact disks, optical disks, magnetic tapes, flash memories,
circuit configurations in Field Programmable Gate Arrays or other
semiconductor devices, or other tangible computer storage medium or
non-transitory medium) encoded with one or more programs that, when
executed on one or more computers or other processors, perform
methods that implement the various embodiments of the technology
discussed above. The computer readable medium or media can be
transportable, such that the program or programs stored thereon can
be loaded onto one or more different computers or other processors
to implement various aspects of the present technology as discussed
above.
[0287] The terms "program" or "software" are used herein in a
generic sense to refer to any type of computer code or set of
computer-executable instructions that can be employed to program a
computer or other processor to implement various aspects of the
present technology as discussed above. Additionally, it should be
appreciated that according to one aspect of this embodiment, one or
more computer programs that when executed perform methods of the
present technology need not reside on a single computer or
processor, but may be distributed in a modular fashion amongst a
number of different computers or processors to implement various
aspects of the present technology.
[0288] Computer-executable instructions may be in many forms, such
as program modules, executed by one or more computers or other
devices. Generally, program modules include routines, programs,
objects, components, data structures, etc. that perform particular
tasks or implement particular abstract data types. Typically the
functionality of the program modules may be combined or distributed
as desired in various embodiments.
[0289] Also, the technology described herein may be embodied as a
method, of which at least one example has been provided. The acts
performed as part of the method may be ordered in any suitable way.
Accordingly, embodiments may be constructed in which acts are
performed in an order different than illustrated, which may include
performing some acts simultaneously, even though shown as
sequential acts in illustrative embodiments.
[0290] All definitions, as defined and used herein, should be
understood to control over dictionary definitions, definitions in
documents incorporated by reference, and/or ordinary meanings of
the defined terms.
[0291] The indefinite articles "a" and "an," as used herein in the
specification and in the claims, unless clearly indicated to the
contrary, should be understood to mean "at least one."
[0292] The phrase "and/or," as used herein in the specification and
in the claims, should be understood to mean "either or both" of the
elements so conjoined, i.e., elements that are conjunctively
present in some cases and disjunctively present in other cases.
Multiple elements listed with "and/or" should be construed in the
same fashion, i.e., "one or more" of the elements so conjoined.
Other elements may optionally be present other than the elements
specifically identified by the "and/or" clause, whether related or
unrelated to those elements specifically identified. Thus, as a
non-limiting example, a reference to "A and/or B", when used in
conjunction with open-ended language such as "comprising" can
refer, in one embodiment, to A only (optionally including elements
other than B); in another embodiment, to B only (optionally
including elements other than A); in yet another embodiment, to
both A and B (optionally including other elements); etc.
[0293] As used herein in the specification and in the claims, "or"
should be understood to have the same meaning as "and/or" as
defined above. For example, when separating items in a list, "or"
or "and/or" shall be interpreted as being inclusive, i.e., the
inclusion of at least one, but also including more than one, of a
number or list of elements, and, optionally, additional unlisted
items. Only terms clearly indicated to the contrary, such as "only
one of" or "exactly one of," or, when used in the claims,
"consisting of," will refer to the inclusion of exactly one element
of a number or list of elements. In general, the term "or" as used
herein shall only be interpreted as indicating exclusive
alternatives (i.e. "one or the other but not both") when preceded
by terms of exclusivity, such as "either," "one of," "only one of,"
or "exactly one of." "Consisting essentially of," when used in the
claims, shall have its ordinary meaning as used in the field of
patent law.
[0294] As used herein in the specification and in the claims, the
phrase "at least one," in reference to a list of one or more
elements, should be understood to mean at least one element
selected from any one or more of the elements in the list of
elements, but not necessarily including at least one of each and
every element specifically listed within the list of elements and
not excluding any combinations of elements in the list of elements.
This definition also allows that elements may optionally be present
other than the elements specifically identified within the list of
elements to which the phrase "at least one" refers, whether related
or unrelated to those elements specifically identified. Thus, as a
non-limiting example, "at least one of A and B" (or, equivalently,
"at least one of A or B," or, equivalently "at least one of A
and/or B") can refer, in one embodiment, to at least one,
optionally including more than one, A, with no B present (and
optionally including elements other than B); in another embodiment,
to at least one, optionally including more than one, B, with no A
present (and optionally including elements other than A); in yet
another embodiment, to at least one, optionally including more than
one, A, and at least one, optionally including more than one, B
(and optionally including other elements); etc.
[0295] In the claims, as well as in the specification above, all
transitional phrases such as "comprising," "including," "carrying,"
"having," "containing," "involving," "holding," "composed of," and
the like are to be understood to be open-ended, i.e., to mean
including but not limited to. Only the transitional phrases
"consisting of" and "consisting essentially of" shall be closed or
semi-closed transitional phrases, respectively, as set forth in the
United States Patent Office Manual of Patent Examining Procedures,
Section 2111.03.
[0296] The described embodiments of the disclosure are intended to
be merely exemplary and numerous variations and modifications will
be apparent to those skilled in the art. All such variations and
modifications are intended to be within the scope of the present
invention as defined in the appended claims.
* * * * *