U.S. patent application number 16/384781 was filed with the patent office on 2019-08-01 for personal health operating system.
This patent application is currently assigned to NANTHEALTH, INC.. The applicant listed for this patent is NANTHEALTH, INC.. Invention is credited to Vasu Rangadass, Ravi Seshadri, Patrick Soon-Shiong.
Application Number | 20190237192 16/384781 |
Document ID | / |
Family ID | 54142374 |
Filed Date | 2019-08-01 |
United States Patent
Application |
20190237192 |
Kind Code |
A1 |
Soon-Shiong; Patrick ; et
al. |
August 1, 2019 |
PERSONAL HEALTH OPERATING SYSTEM
Abstract
A method for facilitating a personal health operating system
(PHOS) is provided in one example embodiment and includes
extracting data into a mobile device that includes a portable
health virtual machine executing the PHOS using a processor couples
to a memory element, generating an N-gram dataset comprising data
indicative and predictive of fitness of an individual, generating,
in the PHOS, an N-gram from the N-gram dataset from the data
according to a data structure indicative and predictive of fitness
of an individual, the fitness including a numerical index
representing a composite effect of various health conditions of the
individual including interdependencies of the health conditions,
generating an N-gram based on the N-gram dataset and calculating
the individual's fitness using the N-gram.
Inventors: |
Soon-Shiong; Patrick;
(Culver City, CA) ; Rangadass; Vasu; (Arlington,
TX) ; Seshadri; Ravi; (Plano, TX) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
NANTHEALTH, INC. |
Culver City |
CA |
US |
|
|
Assignee: |
NANTHEALTH, INC.
Culver City
CA
|
Family ID: |
54142374 |
Appl. No.: |
16/384781 |
Filed: |
April 15, 2019 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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14657679 |
Mar 13, 2015 |
10262759 |
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16384781 |
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61954969 |
Mar 18, 2014 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G16H 50/30 20180101;
G16H 50/20 20180101 |
International
Class: |
G16H 50/20 20060101
G16H050/20; G16H 50/30 20060101 G16H050/30 |
Claims
1-20. (canceled)
21. A method, comprising: generating, in a system comprising at
least one processor and a memory, a dataset according to a data
structure indicative of a fitness of an individual, wherein the
fitness represents a composite effect of various health conditions
of the individual including interdependencies of the health
conditions, the various health conditions including a disease, and
the dataset comprising genomic data of the individual; generating
tokens based on the dataset, at least one of the tokens having a
value from a health record of the individual, the value being
indicative of a medication interaction propensity; calculating the
individual's fitness using the tokens; and providing a
recommendation based on the individual's fitness.
22. The method of claim 21, further comprising exposing the tokens
to an application executing in the system.
23. The method of claim 22, wherein the tokens are exposed through
an application programming interface (API).
24. The method of claim 23, wherein the application interacts
through the API to obtain analysis results on the dataset using the
tokens.
25. The method of claim 24, further comprising generating an alert
if the analysis results indicate a fitness change exceeding a
preconfigured threshold.
26. The method of claim 21, further comprising analyzing the
dataset using the tokens for variation in fitness over time.
27. The method of claim 21, further comprising extracting data into
the system from a sensor.
28. The method of claim 21, further comprising providing a
recommendation based on the fitness calculated from the tokens.
29. The method of claim 21, further comprising initiating a
software action based on the fitness calculated from the
tokens.
30. The method of claim 21, wherein the tokens include an
N-gram.
31. The method of claim 21, wherein the dataset comprises one or
more of: a diagnosis event, a testing event, an imaging event, an
eating event, and a social event.
32. The method of claim 21, wherein the at least one of the tokens
comprises an attribute-value pair.
33. Non-transitory computer readable media that includes
instructions for execution by at least one processor, and when
executed by the at least one processor is operable to perform
operations comprising: generating, in a system comprising at least
one processor and a memory, a dataset according to a data structure
indicative of a fitness of an individual, wherein the fitness
represents a composite effect of various health conditions of the
individual including interdependencies of the health conditions,
the various health conditions including a disease, and the dataset
comprising genomic data of the individual; generating tokens based
on the dataset, at least one of the tokens having a value from a
health record of the individual, the value being indicative of a
medication interaction propensity; calculating the individual's
fitness using the tokens; and providing a recommendation based on
the individual's fitness.
34. The media of claim 33, wherein the operations further comprise
exposing the tokens to an application executing in the system.
35. The media of claim 34, wherein the tokens are exposed through
an API.
36. The media of claim 35, wherein the application interacts
through the API to obtain analysis results on the dataset using the
tokens.
37. media of claim 33, wherein the dataset comprises one or more
of: a diagnosis event, a testing event, an imaging event, an eating
event, or a social event.
38. The media of claim 33, wherein the at least one of the tokens
comprises an attribute-value pair.
39. A system comprising: a memory element to store data; at least
one processor, coupled with the memory element, to execute
instructions associated with the data, wherein the processor and
the memory element cooperate such that the system is configured
for: generating, in a system comprising at least one processor and
a memory, a dataset according to a data structure indicative of a
fitness of an individual, wherein the fitness represents a
composite effect of various health conditions of the individual
including interdependencies of the health conditions, the various
health conditions including a disease, and the dataset comprising
genomic data of the individual; generating tokens based on the
dataset, at least one of the tokens having a value from a health
record of the individual, the value being indicative of a
medication interaction propensity; calculating the individual's
fitness using the tokens; and providing a recommendation based on
the individual's fitness.
40. The system of claim 39, further comprising an application
executing in the system wherein the system is further configured
for exposing the tokens to the application.
41. The mobile device of claim 40, wherein the tokens are exposed
through an API.
42. The mobile device of claim 41, wherein the application
interacts through the API to obtain analysis results on the dataset
using the tokens.
43. The mobile device of claim 39, wherein the dataset comprises
one or more of: a diagnosis event, a testing event, an imaging
event, an eating event, or a social event.
44. The mobile device of claim 39, wherein the at least one of the
tokens comprises an attribute-value pair.
Description
CROSS-REFERENCE TO RELATED APPLICATION
[0001] This application claims the benefit of priority under 35
U.S.C. .sctn. 119(e) to U.S. Provisional Patent Application Ser.
No. 61/954,969, filed on Mar. 18, 2014 and entitled PERSONAL HEALTH
OPERATING SYSTEM, the disclosure of which is hereby incorporated by
reference in its entirety.
TECHNICAL FIELD
[0002] This disclosure relates in general to the field of
healthcare systems and, more particularly, to a personal health
operating system.
BACKGROUND
[0003] "mHealth" is a technology area that broadly encompasses use
of mobile telecommunication and multimedia technologies in health
care delivery. mHealth has emerged as a sub-segment of eHealth,
with use of information and communication technology (ICT), such as
computers, mobile phones, communications satellites, patient
monitors, etc., for health services and information. mHealth
applications typically include use of mobile devices in collecting
community and clinical health data, delivery of healthcare
information to practitioners, researchers, and patients, real-time
monitoring of patient vital signs, and direct provision of care
(via mobile telemedicine). mHealth operates on the premise that
technology integration within the health sector has great potential
to promote better health communication to achieve healthy
lifestyles, improve decision-making by health professionals and
patients, and enhance healthcare quality (e.g., by improving access
to medical and health information and facilitating instantaneous
communication through mobile technology). Moreover, increased use
of mobile technology can reduce health care costs (e.g., by
improving efficiencies in the health care system and promoting
prevention through behavior change communication), and advance
clinical care and public health services through better
communication.
BRIEF DESCRIPTION OF THE DRAWINGS
[0004] To provide a more complete understanding of the present
disclosure and features and advantages thereof, reference is made
to the following description, taken in conjunction with the
accompanying figures, wherein like reference numerals represent
like parts, in which:
[0005] FIG. 1 is a simplified block diagram illustrating a system
according to an example embodiment;
[0006] FIG. 2 is a simplified diagram illustrating example details
of an embodiment of the system;
[0007] FIG. 3 is a simplified sequence diagram illustrating
potential operations that may be associated with an embodiment of
the system;
[0008] FIG. 4 is a simplified sequence diagram illustrating other
potential operations that may be associated with an embodiment of
the system;
[0009] FIG. 5 is a simplified sequence diagram illustrating yet
other potential operations that may be associated with an
embodiment of the system;
[0010] FIG. 6 is a simplified block diagram illustrating other
example details of an embodiment of the system;
[0011] FIG. 7 is a simplified block diagram illustrating yet other
example details of an embodiment of the system;
[0012] FIG. 8 is a simplified flow diagram illustrating example
operations that may be associated with an embodiment of the
system;
[0013] FIG. 9 is a simplified block diagram illustrating yet other
example details of an embodiment of the system;
[0014] FIG. 10 is a simplified block diagram illustrating yet other
example details of an embodiment of the system; and
[0015] FIG. 11 is a simplified block diagram illustrating yet other
example details of an embodiment of the system.
DETAILED DESCRIPTION OF EXAMPLE EMBODIMENTS
Overview
[0016] A personal health operating system (PHOS) is provided in one
example embodiment and includes extracting data into a mobile
device that includes a portable health virtual machine executing
the PHOS using a processor couples to a memory element, generating
an N-gram dataset comprising data indicative and predictive of
fitness of an individual (e.g., user of the mobile device),
generating, in the PHOS, an N-gram from the N-gram dataset from the
data according to a data structure indicative and predictive of
fitness of an individual, the fitness including a numerical index
representing a composite effect of various health conditions of the
individual including interdependencies of the health conditions,
generating an N-gram based on the N-gram dataset and calculating
the individual's fitness using the N-gram.
[0017] As used herein, the term "N-gram" refers to a sequence
(e.g., contiguous or non-contiguous) of N primitives (grams) from a
given collection of data (e.g., N-gram dataset) and representing a
particular health condition combined with state information (e.g.,
progression index for comorbid conditions). The N-gram, which may
be considered metaphorically similar to a gene sequence, can
comprise one or more significant health related events that affect
the individual's overall health. Note that a health related event
comprises an interaction between the individual and externalities
(e.g., objects, people and experiences external to the individual)
in a manner that impacts any health condition of the individual.
Each separate health condition may be represented by a
corresponding separate N-gram. For example, a diabetes health
condition may be represented by a sequence of diagnosis event, test
event, etc. that together comprise the N-gram for the diabetes
health condition.
EXAMPLE EMBODIMENTS
[0018] Turning to FIG. 1, FIG. 1 is a simplified block diagram
illustrating a system 10 according to an example embodiment. System
10 may include a mobile device 12 executing a personal health
operating system (PHOS) 11. Mobile device 12 includes any hardware
wireless communication device that is capable of exchanging
information in a wireless or cellular network environment, and can
include, by way of examples and not as limitations, mobile phones,
smart mobile phones, tablets, phablets, portable navigation
systems, vehicles equipped with wireless communication, and
multimedia devices.
[0019] Data in a cloud 14, including personal data 16 and public
data 18, and sensor data 19 may be extracted (e.g., mined,
retrieved, recovered, received, etc.) by a dataset generate module
20 in mobile device 12 to generate (e.g., derive, produce, create,
develop, etc.) an N-gram dataset 22 from personal data 16 and
public data 18, sensor data 19, and data from an N-gram corpus 23
in cloud 14 according to a data structure indicative and predictive
of fitness of an individual (e.g., user of mobile device 12). Note
that N-gram dataset 22 comprises a collection of separate data
elements related by virtue of their association with the individual
and which can be manipulated as a unit by PHOS 11. N-gram dataset
22 may correspond to a database, table, array, matrix, etc. based
on particular needs.
[0020] As used herein, "fitness" comprises a numerical index (e.g.,
score, value, rate, scalar, vector, etc.) representing a composite
effect of various health conditions of the individual, including
their interdependencies. Note that health conditions includes
illnesses, injuries, impairments, and physical or mental conditions
that affect the health and well-being of the individual. Some
health conditions may be minor and acute, such a temporary headache
from a hangover; other health conditions may be major and chronic,
such as coronary heart disease. Some health conditions may have no
discernable effects until an event triggers reactions, such as bee
sting allergy; other health conditions may have continuous effects,
such as rheumatoid arthritis with long term deleterious effects on
bone joints.
[0021] The fitness can indicate and/or predict health, wellbeing,
vigor, stamina, strength, health condition, etc. of the individual
based on the individual's health conditions and their
interdependencies. For example, a person with optimal health may
have blood pressure readings, blood sugar readings, pulse rate,
blood alcohol level, etc. in a healthy range, resulting in high
fitness; whereas a drunk consumer at a bar may have high blood
alcohol level, resulting in low fitness. In another example, a
person with diabetes only may have a certain fitness, whereas
another person with both diabetes and hypertension may have a
different fitness due to interdependencies of diabetes and
hypertension. In yet another example, a person taking medication
for epilepsy may experience a progressive degrading liver condition
due to side effects of the medication and the person's physiology,
and the fitness may comprehend such dependencies.
[0022] In various embodiments, an optimum fitness based on various
disparate data values may be preconfigured in PHOS 11 for the
individual. Different individuals may have different preconfigured
optimum fitness based on personal medical history, or demographic
information (e.g., age, gender, etc.), or other suitable
parameters. In some embodiments, N-gram corpus 23 may include
fitness indicative data pertaining to a population (e.g., group,
plurality, etc.) of individuals; the data therein may be used to
calculate the optimum fitness for the individual based on the
individual's personal characteristics in relation to the population
data.
[0023] Merely as examples, and not as limitations, data in N-gram
dataset 22 can include anti-bio grams (e.g., result of a laboratory
testing for sensitivity of an isolated bacterial strain to
different antibiotics), biomarkers (e.g., measured characteristic
used in the examination of normal biological processes, pathogenic
processes, or pharmacologic responses to a therapeutic
intervention), meta-markers (e.g., markers of a marker; for
example, fever is a marker of cytokines, which itself is a marker
of hypotension due to septicemia), proteomics (e.g., study of
proteins), genomics (e.g., study of genomes), location,
consumer/retail behavior (e.g., credit card charges, shopping
sites, etc.), and social networking behavior (e.g., contacts,
personal information, etc.). Virtually any data that can be used to
indicate fitness and/or predict fitness may be included in N-gram
dataset 22 within the broad scope of the embodiments.
[0024] In various embodiments, such data may be readily available
through cloud 14 without further analysis; in other embodiments,
such data may be determined from analysis of available information
in cloud 14. For example, an individual's social network may be
analyzed to identify related family members; information from
social media interactions with such family members may be analyzed
to determine or augment a family history of health conditions; such
information may be added to genomics data in N-gram dataset 22;
etc. Any suitable method to create N-gram dataset 22 may be
included within the broad scope of the embodiments.
[0025] Virtually any suitable data that can be accessed through
cloud 14 and from sensors may be used to generate N-gram dataset 22
are included within the broad scope of the embodiments. In a
general sense, data, including personal data 16, public data 18,
and sensor data 19 refers to any type or modality of numeric, text,
voice, video, or script data, or any type of source or object code,
or any other suitable information in any appropriate format that
may be communicated from one point to another in electronic devices
and/or networks. Note that sensors generating sensor data 19 may be
connected to mobile device 12 through appropriate communication
channels (e.g., embedded, wired, or wireless (e.g., near field
communication, Bluetooth, etc.) suitably sufficient to allow
dataset generate module 20 to access and obtain the data
therefrom.
[0026] For example, personal data 16 can include clinical data,
financial data, social network data, consumer data, etc. which can
be used to generate N-gram dataset 22. For example, clinical data
can include information (e.g., facts) related to diagnosis and
treatment of a current or potential health condition (e.g.,
disease, diabetes, obesity, aging, etc.), demographic information
(e.g., age, weight, gender) that may be relevant to diagnosis and
treatment of a current or potential health condition, data
generated during clinical encounters (e.g., visit at physician's
office, clinics, hospitals, laboratory testing, in-home testing),
data generated from medical sensors and equipment, etc. Financial
data can include bank statements, credit card charges, stock
trades, etc. Social network data can include social networking
sites, data input by the individual or his/her contacts, etc.
Consumer data can include shopping sites, visited shops, retail
memberships, shopping preferences, purchased goods, eating and
drinking outlets like restaurants and bars and sports and
recreation areas parks, fitness facilities, tennis courts), etc. In
another example, public data 18 can include any publicly available
medical database of diseases, health conditions, treatments, etc.,
environment data (e.g., weather, terrain, etc.), news, etc.
[0027] Substantially all personal data 16 may be extracted upon
receiving consent to do so from the relevant individual/owner of
personal data 16. In some embodiments, dataset generate module 20
may ask for user permission to extract (e.g., download) a specific
data at a time of extraction, possibly within a homomorphic
encryption environment. In other embodiments, dataset generate
module 20 may ask for user permission to extract substantially all
accessible personal data 16 during installation of PHOS 11. In yet
other embodiments, the individual may implicitly provide permission
by giving access to personal data 16 in cloud 14 (e.g., through
appropriate user names, passwords, security certificates,
etc.).
[0028] A memory element 24 and a processor 25 may be used to
execute the operations of system 10. PHOS 11 may also include an
N-gram generate module 26 that can generate an N-gram 28 from
N-gram dataset 22. N-gram 28 may be exposed to applications 30
through a PHOS application programming interface (API) 32. An
analysis module 34 may analyze N-gram dataset 22 and N-gram 28,
according to mobile device settings 36 (and applications 30, as
needed and based upon particular
specifications/configurations).
[0029] An alert notification module 38 may be configured to alert
the individual when predictions and/or analysis results based on
N-gram 28 indicate a fitness change exceeding a preconfigured
(e.g., predetermined) threshold. In an example embodiment, the
threshold may be set around an optimum fitness of the individual,
for example, as a range (e.g., optimum fitness .+-.2.sigma., where
.sigma. is the standard deviation of the data). Any deviation from
the optimum fitness by more than the threshold can result in an
alert. In another example embodiment, the threshold may range
around trends in the fitness. For example, a decreasing fitness
trend larger than a preconfigured threshold rate may result in an
alert. In some embodiments, the alert can be positive in nature
(e.g., indicating that the individual is "doing a good job"); in
other embodiments, the alert can be negative in nature (e.g.,
indicating "don't do that"). Any suitable form of alert (e.g.,
text, pop up window, beeps and other sounds, etc.) may be used
within the broad scope of the embodiments.
[0030] According to various embodiments, dataset generate module 20
may format data in N-gram dataset 22 according to a particular data
structure, such as:
[coded-longitudinal-time].[coded-condition-category].[coded-phenotype-dat-
a].[coded-evidence-index].[condition-aggrevation-index]. The data
structure may serve to provide a virtual view of N-gram 28 with an
anchor for a given dominant (e.g., requested) clinical condition
(e.g., health condition, disease, etc.).
[0031] In various embodiments, the data structure for N-gram
dataset 22 can overcome existing limitations for representations of
constituents versus dependency, where existing representations use
a "data mart" approach to create a "constituent model" thereby
repeating information redundantly without elegantly representing
the impact of inter-dependency of a single health related event to
multiple conditions. In various embodiments, the data structure may
be formatted to capture the essence of a health related event in a
coded form over longitudinal time. For example, N-gram 28 generated
for the health related event can capture significant aspects of the
health related event based on the coded N-gram dataset 22.
[0032] Note that examples of health related events include
diagnosis events (e.g., where diagnosis of a medical condition is
made), testing events (e.g., where a biological parameter of the
individual is tested), imaging events (e.g., where various kinds of
health care related images of the individual are taken), eating
events (e.g., where the individual imbibes food and drinks that
affect the individual's health conditions), social events (e.g.,
where the individual is in a social setting that can affect health
conditions), or any other events that affect the health condition
of the individual (including the user of mobile device 12).
[0033] In some embodiments, some parameters in N-gram dataset 22
may not occur frequently relative to other parameters. For example,
for a person with high blood pressure, but normal blood sugar,
parameters indicative of the high blood pressure condition,
including blood pressure readings, blood pressure medications,
stroke events, etc., may occur more frequently than blood sugar
readings, diabetes events or hypoglycemia events. Consequently, the
resolving power of the predictor based on the low frequency
parameters may have low statistical confidence. Low statistically
confidence can be overcome in some embodiments by augmenting the
statistics with N-gram 28 from similar individuals having similar
attributes (e.g., age, gender, health state, geo-location, etc.),
for example, gleaned from N-gram corpus 23.
[0034] Turning to the data structure of data in N-gram dataset 22,
assume, merely as an example and not as a limitation, that data in
N-gram dataset 22 includes timestamps coded as universal time code
(UTC); location coded as a combination of latitude and longitude;
various health conditions coded appropriately (e.g., cardiovascular
condition coded as CARDIO, diabetic condition coded as ENDOCR,
etc.); phenotype data coded appropriately (e.g., hypertension coded
as HT with corresponding value of none, low, or high; diabetics
coded as DB with corresponding value of none, low, or high, etc.);
evidence data coded according to fields (e.g., BPDATA field with
corresponding blood pressure value, A1CDATA field with
corresponding A1C blood test values); etc. In some embodiments,
N-gram generate module 26 may parse through N-gram dataset 22
according to a filter represented by N-gram 28 and extract
appropriate coded data therein to feed into analysis module 34.
[0035] In an example, a diagnosis event of "hypertension" may be
represented by N-GRAM 28 as follows:
UTC.LAT:LONG.CARDIO.HT:HIGH.[BPDATA].NIH:HT01.[Computed Aggravation
Index based on personal+family+social history], where HT: HIGH
represents a coded phenotype data for hypertension with a value of
"high"; [BPDATA] represents a coded evidence index, indicating
blood pressure data and corresponding value; NIH:HT01 represents a
uniform code for hypertension, stage 1; and [Computed Aggravation
Index based on personal+family+social history] represents a
computed aggravation index indicating the extent of aggravation
from contributing factors such as personal history, family history
and social history (e.g., a smoker may aggravate his medical
condition due to his personal history of smoking and the smoker's
aggravation index from personal history may be higher than a
non-smoker's corresponding aggravation index).
[0036] In another example, a diagnosis event of "diabetes" may be
represented by N-gram 28 as follows:
UTC.LAT:LONG.ENDOCR.DB:HIGH.[A1CDATA].NIH:DB01.[Computed
Aggravation Index based on personal+family+social history], where
UTC represents a coded-longitudinal time; LAT: LONG represents a
coded location category; ENDOCR represents the condition category
for endocrine health; DB: HIGH represents a coded phenotype data
for diabetes, with a value of "high"; [A1CDATA] represents a coded
evidence index, indicating A1C data and corresponding value;
NIH:DB01 represents a uniform code for diabetes, stage 1; and
[Computed Aggravation Index based on personal+family+social
history] represents the computed aggravation index indicating the
extent of aggravation from contributing factors such as personal
history, family history and social history.
[0037] For a person with both hypertension and diabetes, the
aggravation index on occurrence of the second event in time may be
computed based on both health conditions (e.g., aggravation index
may be higher for a patient with both conditions than for another
patient with only one of the conditions). In some embodiments, a
comorbidity corpus can be used to track a comorbid, holistic or
dominant condition trajectory and to compute the aggravation
index.
[0038] In some embodiments, for a repeat of a health related event,
N-gram 28 may capture an optimistic concurrency control (OCC) of
the time and location, for example, as
UTC.LAT:LONG.ENDOCR.DB:HIGH.[A1CDATA].NIH:DB01.[Computed
Aggravation Index based on personal+family+social
history]:[OCC:[TimeStamp:Lat:Long]]. Thus, transactions
representing health related events that repeat in different time or
locations may be identified as such through OCC algorithms. Note
that any suitable OCC algorithm may be implemented in PHOS 11
within the broad scope of the embodiments.
[0039] In a general sense, N-gram 28 can be used for efficient
approximate matching. For example, by converting a sequence of data
to N-gram 28, the data can be embedded in a vector space, thus
allowing the sequence to be compared to other sequences in an
efficient manner. Vectors can create efficiencies for matching
(e.g., tree structures) or for distance comparison (e.g., Euclidean
distance, Hamming distance, etc.) In a general sense, vectors
represented by N-gram 28 may exist in well-defined namespace.
[0040] Example applications of N-gram 28 include statistical
language modeling (e.g., for textual mapping and comparisons to
determine associations and classifications, extracting concepts
from medical reports and narratives, phrase and topic discovery),
database searching (e.g., searching molecular databases for genomic
and proteomic sequences), sequence generation (e.g., for heartbeat
analysis, genomic classification) etc. In general, many indexing,
retrieval, and comparison methods can be based on counting or
cataloguing N-gram 28 in streams of symbols.
[0041] In the various embodiments, N-gram 28 can be indicative and
predictive of fitness of the individual to whom N-gram dataset 22
pertains. In some embodiments, different N-grams 28 may be
generated for different aspects of fitness. For example, N-gram A
may be indicative of diabetics conditions of the individual; N-gram
B may be indicative of heart conditions of the individual; N-gram C
may be indicative of the individual's stamina; N-gram D may be
indicative of the individual's medication interaction propensities
and allergies; and so on. Virtually any number and type of N-gram
28 may be generated within the broad scope of the embodiments.
[0042] In some embodiments, N-gram generate module 26 may query
sequences of n data points in N-gram dataset 22. Estimating the
number of distinct N-grams can be a view-size estimation problem.
In an example embodiment, N-gram generate module 26 may implement a
one-pass one-hash algorithm for accurate estimates of the number of
distinct N-grams when the hashing is sufficiently independent
(e.g., no two N-grams are hashed to the same value). In an example
embodiment, the N-gram hashing function extracts and counts
occurrences of patterns of n consecutive items (e.g., sliding
window of size n) from a sequence string. For example, term
frequency-inverse document frequency (tf-idf) (e.g., a numerical
statistic that reflects how important a word is to a corpus) may be
used to determine frequency of specific patterns in N-gram dataset
22. Any suitable N-gram extraction method may be implemented by
N-gram generate module 26 to generate N-gram 28 within the broad
scope of the embodiments.
[0043] To count the N-grams, a given pattern p.di-elect cons.P can
be instantiated for specific candidate tuples (x;y) as p(x;y) to
yield an N-gram string. For instance, a pattern such as "<X>:
<Y>" can be instantiated with a tuple <glucose level,
105> to yield an N-gram A "glucose level: 105". N-gram dataset
22 may be consulted to obtain raw or manipulated frequency
information f(p(x;y)) that reveals how frequently N-gram A occurs
in the data. In some embodiments, N-gram generate module 26 takes a
string (e.g., data in N-gram dataset 22) as input, breaks it into a
list of units ("tokens"), and finds each successive group of n
consecutive or non-consecutive tokens in N-gram dataset 22.
[0044] In some embodiments, the data in N-gram dataset 22 may be
reviewed and portions of the data may be assigned tokens
representing health related events associated with the respective
portions. In some embodiments, N-gram 28 may be generated according
to a desired (e.g., requested) ordering. For example, time ordering
may be used for longitudinal analysis. Other orderings (e.g.,
location orderings) may also be applied based on particular needs.
In some embodiments, token T1 in N-gram 28 may generate many
possible candidates of data C(T1). The candidates C(T1) can be
ranked by frequency in N-gram dataset 22, severity, and/or other
parameters of interest. The fitness of the individual can be
calculated as a function of possible candidates C(T1) (e.g.,
fitness=.SIGMA.a.sub.jT.sub.j, T.sub.j.di-elect cons.C(T1);
etc.)
[0045] In various embodiments, a plurality of N-gram 28 may be
generated and N-gram dataset 22 analyzed accordingly, with each
N-gram 28 predictive (or indicative) of a different health
condition. A plurality of N-gram strings may be used substantially
simultaneously to analyze data in N-gram dataset 22. In some
embodiments, a set of predicated N-grams can provide a leading
indicator of possible events. For example, analysis of N-gram
dataset 22 using N-gram A can indicate that N-gram B, a predicate
of N-gram A, indicates heart attack for the individual.
[0046] In a general sense, N-gram 28 may be useful in predictions
with historical information scaled based on "N" (e.g., number of
grams in N-gram 28). In some embodiments, the prediction may be
based on a 1-gram analysis, 2-gram analysis, 3-gram analysis and so
on. The statistical confidence of predication can be measured
consistency of the analyses (e.g., if predication is nearly the
same for N analyses, then confidence is high).
[0047] In some embodiments, a set of possible health related events
that could take place in a particular situation may be defined in
an event vocabulary by dataset generate module 20 in N-gram dataset
22. The vocabulary may comprise, by way of examples and not as
limitations, "heart attack," "stroke," "hypothermia,"
"anaphylaxis," "hypoglycemia," "blood alcohol level," etc. Each
gram in the vocabulary could be a scalar or unitary value (e.g.,
"heart attack, broken leg, GUID, etc.) in some embodiments. In
other embodiments, each gram could have multiple values. For
example, the health condition name and severity (hypertension:
high) may comprise a single gram facilitating measuring N-gram 28
by name and significance or severity or value or equivalent. Each
gram, N of which comprise an N-gram, may be normalized so that
system 10 operates in the same namespace irrespective of the gram
type. In some embodiments, each gram may comprise an
attribute-value pair (e.g., {vigor::34}).
[0048] In various embodiments, N-gram 28 can built up statistical
models of health related events based on populations. For example,
a statistical model based on gender may utilize a particular N-gram
28; another statistical model based on age may utilize another
N-gram 28; yet another statistical model based on location may
utilize yet another N-gram 28; yet another statistical model based
on genomic attributes may utilize yet another N-gram 28; and so on.
Moreover, N-gram 28 generated in mobile device 12 may pertain to a
specific individual (e.g., personalized N-gram), whereas other
N-grams generated based on N-gram corpus 23 in cloud 14 may
comprise a cohort N-gram with respect to groups of people.
[0049] Turning to the infrastructure of system 10, mobile device 12
may operate in various wireless communication networks such as Code
division multiple access (CDMA), Time division multiple access
(TDMA), Frequency Division Multiple Access (FDMA), Orthogonal
Frequency-Division Multiple Access (OFDMA), Single Carrier
Frequency-Division Multiple Access (SC-FDMA) and other networks. A
CDMA network may implement a radio technology, such as UTRA,
Telecommunications Industry Association's (TIA's) CDMA2000.RTM.,
and the like. The UTRA technology includes Wideband CDMA (WCDMA)
and other variants of CDMA. The CDMA2000.RTM. technology includes
the IS-2000, IS-95 and IS-856 standards from the Electronics
Industry Alliance (EIA) and TIA.
[0050] The TDMA network may implement a radio technology, such as
Global System for Mobile Communications (GSM). An OFDMA network may
implement a radio technology, such as Evolved UTRA (E-UTRA), Ultra
Mobile Broadband (UMB), IEEE 802.11 (Wi-Fi), IEEE 802.16 (WiMAX),
WiGIG 802.11ad, IEEE 802.20, Flash-OFDMA, and the like. The UTRA
and E-UTRA technologies are part of Universal Mobile
Telecommunication System (UMTS). 3GPP Long Term Evolution (LTE) and
LTE-Advanced (LTE-A) are newer releases of the UMTS that use
E-UTRA. UTRA, E-UTRA, UMTS, LTE, LTE-A and GSM are described in
3GPP documents. CDMA2000.RTM. and UMB are described in 3GPP2
documents. The techniques described herein may be used for the
wireless networks and radio access technologies mentioned above, as
well as other wireless networks and radio access technologies.
Mobile device 12 may also communicate using near field
communication (NFC), Bluetooth, RFID, Wi-Fi, or other wireless
technologies (e.g., IEEE 802.11x).
[0051] Mobile device 12 may also implement other mobile operating
systems that can combine features of a personal computer operating
system with other features, including a touchscreen, cellular,
Bluetooth, Wi-Fi, GPS mobile navigation, camera, video camera,
speech recognition, voice recorder, music player, near field
communication, etc. In a general sense, the mobile operating system
includes a user-facing software platform supplemented by a second
low-level real-time operating system that operates the radio and
other hardware.
[0052] Data exchanges among mobile device 12 and cloud 14 and
sensors and other devices can also be conducted over a
packet-switched network, the Internet, LAN, WAN, VPN, or other type
of packet switched network; a circuit switched network; cell
switched network; or other type of network. Elements of FIG. 1 may
be coupled to one another through one or more interfaces employing
any suitable connection (wired or wireless), which provides a
viable pathway for electronic communications. Additionally, any one
or more of these elements may be combined or removed from the
architecture based on particular configuration needs.
[0053] The network in which mobile device 12 communicates offers a
communicative interface between various network components, and may
include any local area network (LAN), wireless local area network
(WLAN), metropolitan area network (MAN), Intranet, Internet,
Extranet, wide area network (WAN), virtual private network (VPN),
or any other appropriate architecture or system that facilitates
communications in a network environment. The network may implement
any suitable communication protocol for transmitting and receiving
data packets within system 10. The architecture of the present
disclosure may include a configuration capable of TCP/IP, TDMA,
and/or other communications for the electronic transmission or
reception information in a network. The architecture of the present
disclosure may also operate in conjunction with any suitable
protocol, where appropriate and based on particular needs. In
addition, gateways, routers, switches, and any other suitable nodes
(physical or virtual) may be used to facilitate electronic
communication between various nodes in the network.
[0054] Note that the numerical and letter designations assigned to
the elements of FIG. 1 do not connote any type of hierarchy; the
designations are arbitrary and have been used for purposes of
teaching only. Such designations should not be construed in any way
to limit their capabilities, functionalities, or applications in
the potential environments that may benefit from the features of
system 10. It should be understood that system 10 shown in FIG. 1
is simplified for ease of illustration.
[0055] In various embodiments, PHOS 11 is configured to interface
with the mobile operating system of mobile device 12 to enable
appropriate use of memory element 24, processor 25 and various
mobile device settings 36, among other functions. Mobile device 12
also includes a suitable user interface, including a touchscreen,
data interface, web interface, authentication portal, and other
objects and modules to facilitate the operations described herein.
In various embodiments, PHOS 11 may comprise a firmware executing
on mobile device 12 with access permissions to operate the hardware
and software. In other embodiments, PHOS 11 may comprise a
collection of software executing on mobile device 12 with access
permissions to operate the hardware and software as indicated by
the individual (e.g., individual gives permission to PHOS 11 to use
the sensors, data, etc. as needed) and that provides common
services for applications 30. In some embodiments, PHOS 11 may
include firmware (e.g., device drivers, kernels, etc.) and software
executing in mobile device 12.
[0056] Cloud 14 is a collection of hardware and software forming a
shared pool of configurable computing resources (e.g., networks,
servers, storage, applications, services, etc.) that can be
suitably provisioned to provide on-demand self-service, network
access, resource pooling, elasticity and measured service, among
other features. Cloud 14 may be deployed as a private cloud (e.g.,
infrastructure operated by a single enterprise/organization),
community cloud (e.g., infrastructure shared by several
organizations to support a specific community that has shared
concerns), public cloud (e.g., infrastructure made available to the
general public), or a suitable combination of two or more disparate
types of clouds. In some embodiments, cloud 14 may be managed by a
cloud service provider, who can provide subscribers (e.g., mobile
device 12) with at least access to cloud 14, and authorization to
use cloud resources in accordance with predetermined service level
agreements.
[0057] Sensors generating sensor data 19 can include camera, GPS
location sensor, health monitoring equipment, and any other device
that can communicate with mobile device 12 (e.g., by embedded,
wired or wireless means) and provide data suitable to be included
in N-gram dataset 22. Other examples of sensors that generate
sensor data 19 can include pulse-oximeter, micro-spirometer,
thermometer, peroxide sensor, etc.
[0058] In some embodiments, applications 30 may be installed and
may execute on mobile device 12. As used herein, the term
"application" can be inclusive of an executable file comprising
instructions that can be understood and processed on a computer,
and may further include library modules loaded during execution,
object files, system files, hardware logic, software logic, or any
other executable modules. In other embodiments, applications 30 may
be installed on a remote network device with access to mobile
device 12 through a suitable network (e.g., wireless network). Note
that virtually any number of applications 30 may be installed and
may execute in mobile device 12 within the broad scope of the
embodiments.
[0059] In some embodiments, analysis module 34 may be used to
analyze N-gram 28, for example, over time, varying fitness
parameters (e.g., stored in N-gram dataset 22), etc. Analysis
module 34 can analyze fitness, for example, to illustrate effects
of certain events (e.g., clinical interventions, diet, exercise,
etc.) on fitness. In some embodiments, analysis module 34 can
provide a snapshot of the individual's fitness at a certain instant
of time; in other embodiments, analysis module 34 can provide a
time-varying graph of the individual's fitness over a specified
time period (e.g., month); in yet other embodiments, analysis
module 34 can predict fitness in the future based on fitness in the
present (or the past) based on individual behavior, and other
parameters. Analysis module 34 can provide analysis of N-gram 28 to
determine medication interactions, effect of certain dietary
ingredients, safe dosages of alcohol, etc. Virtually any suitable
analysis of N-gram 28 relevant to fitness and using data in N-gram
dataset 22 may be performed by analysis module 34 within the broad
scope of the embodiments.
[0060] In certain embodiments, analysis module 34 may be
preconfigured with certain capabilities (e.g., analyze N-gram 28
over time, and for certain pre-determined events). Capabilities of
analysis module 34 may be expanded by interactions with
applications 30 through PHOS API 32 suitably. For example,
applications 30 may cause analysis module 34 to analyze N-gram 28
for particular variations, data, trends, etc.
[0061] Note that the numerical and letter designations assigned to
the elements of FIG. 1 do not connote any type of hierarchy; the
designations are arbitrary and have been used for purposes of
teaching only. Such designations should not be construed in any way
to limit their capabilities, functionalities, or applications in
the potential environments that may benefit from the features of
system 10. It should be understood that the system 10 shown in FIG.
1 is simplified for ease of illustration.
[0062] Turning to FIG. 2, FIG. 2 is a simplified diagram
illustrating example details according to an embodiment of system
10. N-gram dataset 22 may comprise one or more data items D.sub.1 .
. . D.sub.M, which may be used to generate one or more N-grams
28(1), 28(2), which may, in turn, be used to indicate, derive, or
predict fitness 40(1)-40(3). For example, N-gram 28(1) may be
indicative of fitness A 40(1), which relates to the diabetics
condition of the individual (by way of example, and not as a
limitation). A change in N-gram 28(1) may indicate a change in
corresponding fitness A 28(1). For example, N-gram 28(1) may
specify "glucose level: 95;" N-gram 28(1) may change to "glucose
level: 110" based on sensor data 19 after a glucose test using one
or more sensors. Fitness A 28(1) derived from the changed N-gram
28(1) may indicate a change in diabetics condition. In another
example, N-gram 28(2) may be indicative of two fitness
characteristics, fitness B 40(2) and fitness C 40(3), which can
represent, by way of example and not limitation, heart health and
stamina. Virtually any suitable N-gram 28 may be generated from
N-gram dataset 22; the generated N-gram 28 may be used to represent
any number of corresponding fitness (e.g., 40(1)-40(3)).
[0063] In some embodiments, any updates to data in N-gram dataset
22 may trigger generation or modification of N-grams 28(1)-28(2).
In other embodiments, N-grams 28(1)-28(2) may be generated or
modified at predefined periodic intervals. In yet other
embodiments, N-grams 28(1)-28(2) may be generated or modified at
individual request. In yet other embodiments, N-grams 28(1)-28(2)
may be generated or modified according to instructions from
applications 30.
[0064] Turning to FIG. 3, FIG. 3 is a simplified sequence diagram
illustrating example operations 50 that may be associated with
embodiments of system 10. At 52, data from N-gram dataset 22 may be
accessed by N-gram generate module 26 to generate N-gram 28. At 54,
analysis module 34 may use N-gram 28 to derive fitness F.sub.0 40
at time T.sub.0. At 56, updated location data (e.g., indicating a
bar) may be extracted by dataset generate module 20, populated in
N-gram dataset 22, and used by N-gram generate module 26. At 58,
updated consumer data (e.g., indicating credit card charge for
alcohol) may be extracted by dataset generate module 20, populated
in N-gram dataset 22, and used by N-gram generate module 26 to
generate N-gram 28.
[0065] At 60, analysis module 34 may use the updated N-gram 28 to
derive fitness F.sub.1 40 at time T.sub.1. Analysis module 34 may
determine that F.sub.1 is less than F.sub.0, indicating a
deteriorating fitness. At 62, mobile device settings 36 may be
accessed to determine appropriate settings (e.g., preconfigured
settings may indicate alerting a particular contact (e.g., spouse)
if degrading fitness is sensed. At 64, analysis module 34 may
access N-gram dataset 22 to retrieve social networking data (e.g.,
contact's phone number). At 66, analysis module 34 may generate and
send an alert to the contact.
[0066] Turning to FIG. 4, FIG. 4 is a simplified sequence diagram
illustrating example operations 70 that may be associated with
embodiments of system 10. At 72, data from N-gram dataset 22 may be
accessed by N-gram generate module 26 to generate N-gram 28. At 74,
analysis module 34 may use N-gram 28 to derive fitness F.sub.0 40
at time T.sub.0. At 76, updated location data (e.g., indicating a
restaurant) may be extracted by dataset generate module 20,
populated in N-gram dataset 22, and used by N-gram generate module
26.
[0067] At 78, updated consumer data (e.g., indicating credit card
charge for food) may be extracted by dataset generate module 20,
populated in N-gram dataset 22, and used by N-gram generate module
26 to generate N-gram 28. At 80, analysis module 34 may use the
updated N-gram 28 to derive fitness F.sub.1 40 at time T.sub.1.
Analysis module 34 may determine that F.sub.1 is less than F.sub.0,
indicating a deteriorating fitness. The deteriorating fitness may
be caused by eating the restaurant food. At 82, analysis module 34
may generate a low rating for the restaurant based on the
deteriorated fitness.
[0068] Turning to FIG. 5, FIG. 5 is a simplified sequence diagram
illustrating example operations 90 that may be associated with
embodiments of system 10. At 92, data from N-gram dataset 22 may be
accessed by N-gram generate module 26 to generate N-gram 28. At 94,
analysis module 34 may use N-gram 28 to derive fitness F.sub.0 40
at time T.sub.0. At 96, updated clinical data (e.g., indicating
medication prescribed by physician) may be extracted by dataset
generate module 20, populated in N-gram dataset 22, and used by
N-gram generate module 26.
[0069] At 98, updated clinical data at time T.sub.1 (e.g.,
indicating laboratory test results after medication) may be
extracted by dataset generate module 20, populated in N-gram
dataset 22, and used by N-gram generate module 26 to generate
N-gram 28. At 100, analysis module 34 may use the updated N-gram 28
to derive fitness F.sub.1 40 at time T.sub.1. Analysis module 34
may determine that F.sub.1 is better than F.sub.0, indicating
increased fitness. At 102, analysis module 34 may predict better
fitness with continued medication.
[0070] Turning to FIG. 6, FIG. 6 is a simplified block diagram
illustrating example details of an embodiment of system 10. N-gram
28 may be exposed by PHOS API 32 to application 30. In various
embodiments, PHOS API 32 may expose various APIs to applications
30. By way of examples and not as limitations, the APIs may
include, "getNgram(*context), zeroNgram(NgramID), cmpNgram(Ngram1,
Ngram2), deleteNgram(NgramID), addEventToCorpus(EventID),
addNgramToCorpus( ), etc.
[0071] By way of example, and not as a limitation, a specific
example application A 30 may be configured to generate addresses
106 of health care providers in a local geographic area relevant to
fitness derived from N-gram 28. In another example, application 30
may interact with analysis module 34 through PHOS API 32, and
instruct analysis module 34 to analyze effect 107 of a particular
dietary ingredient on N-gram 28 and fitness of the individual. The
results may be provided to application 30 for further analysis and
use, based on the application capabilities. Virtually any
appropriate, user-consented use of N-gram 28 and analysis module 34
may be facilitated by PHOS API 32 within the broad scope of the
embodiments.
[0072] Turning to FIG. 7, FIG. 7 is a simplified diagram
illustrating an example graph 108 of time varying fitness 40
according to an embodiment of system 10. In some embodiments,
analysis module 34 may analyze N-gram 28 over time, and generate
graph 108, which may be displayed to the individual on the mobile
device touchscreen. In some embodiments, health related events
(e.g., event 1, event 2, event 3, event 4, etc.) may be displayed
or located on the graph to show its impact on fitness. Analysis
module 34 may also provide a prediction 109 for a future time based
on the present (and past) fitness and predicted individual behavior
in the future time period. In some embodiments, the N-gram analysis
can predict the next expected gram to occur in a sequence based on
the statistical (e.g., frequency) of the sequence and subsequent
(or preceding) gram in N-gram dataset 22 (or N-gram corpus 23). For
example, if an initial N-gram analysis using a first sequence
A.B.C.D (e.g., corresponding to hypertension related health events)
indicates that a second sequence A.B.C.D.E (e.g., where E
corresponds to potential diabetes related health events given a
condition of hypertension) occurs with high frequency in N-gram
dataset 22, the analysis may predict that the individual may have a
tendency to be potentially diagnosed with diabetes in the
future.
[0073] Turning to FIG. 8, FIG. 8 is a simplified flow diagram
illustrating example operations 110 that may be associated with an
embodiment of system 10. At 112, data (e.g., personal data 16,
public data 18 and sensor data) may be extracted from cloud 14 and
sensors into mobile device 12. At 114, dataset generate module 20
may derive N-gram dataset 22. At 116, N-gram generate module 26 may
generate N-gram 28 from N-gram dataset 22. At 118, PHOS API 32 may
expose N-gram 28 to applications 30. At 120, analysis module 34 may
derive fitness from N-gram. Various actions may follow the
analysis. For example, at 122, analysis module 34 may provide
recommendations based on fitness. At 124, analysis module 34 may
generate actions (e.g., alerting contact) based on fitness. At 126,
the fitness may be stored for predictive analysis. At 128, future
fitness based on variation in N-gram dataset 22 may be predicted.
Various other operations may be performed by analysis module
34.
[0074] Turning to FIG. 9, FIG. 9 is a simplified block diagram
illustrating example details of another embodiment of system 10.
Mobile device 12, which includes processor 25 and memory element 24
may additionally comprise a mobile device operating system (OS)
150. In one example embodiment, mobile device OS 150 may comprise
Android.TM. OS; in another example embodiment, mobile device OS 150
may comprise iPhone OS (iOS.TM.); in yet another example
embodiment, mobile device OS 150 may comprise Blackberry.TM. OS.
Various other examples of mobile device OS may be included within
the broad scope of the embodiments.
[0075] A portable health virtual machine 152 may execute on mobile
device OS 150. In a general sense, a virtual machine (VM) is a
software implementation of a computing device (e.g., personal
computer, server, laptop, smartphone, etc.) that executes programs
similar to the physical (e.g., corporeal) computing device. Example
virtual machines include the Java virtual machine, Xen, Oracle.RTM.
VM VirtualBox, just to name a few. In various embodiments, PHOS 11
may provide a suitable OS for operations (e.g., procedures,
functions, processes, etc.) executing in (or by) portable health
virtual machine 152. An encrypted PHOS/health N-Grams storage
repository 154 and a PHOS API library 156 may be included in
portable health virtual machine 152.
[0076] PHOS API library 156 may expose various API's to
applications 30. In addition, PHOS API library 156 comprises a
collection of implementations of behavior (e.g., written in terms
of a computer language) that has a well-defined interface by which
the behavior is invoked. In addition, the behavior is configured
for reuse by multiple independent programs executing in PHOS 11. A
specific program can invoke the library-provided behavior via a
mechanism of the applicable language (e.g., PHOS API function
calls). PHOS API library 156's code may be organized so that it can
be used by multiple applications that have no connection to each
other.
[0077] Encrypted PHOS/health N-grams storage repository 154 may
comprise a repository of various health related data pertaining to
the individual, including formatted data of N-gram dataset 22,
N-gram 28, and other unformatted data comprising personal data 16,
public data 18, and sensor data 19. Encrypted PHOS/health N-grams
storage repository 154 may make the data therein available in an
encrypted manner to applications 30 through API's. For example,
getBloodPressure(DATE) operation from a particular application A
may allow access to blood pressure data on the requested date
(DATE) from encrypted PHOS/health N-grams storage repository 154.
Because the data stored in encrypted PHOS/health N-grams storage
repository 154 is encrypted, access to the repository through
mechanisms outside the API or PHOS 11 may be ineffective, thereby
providing data security.
[0078] Turning to FIG. 10, FIG. 10 is a simplified block diagram
illustrating example details of an embodiment of system 10.
Encrypted PHOS/health N-grams storage repository 154 can include a
N-grams predictive sequence evidence repository 160, a N-grams
sequence repository 162, a longitudinal health vector storage 164,
a longitudinal personal health related events meta data repository
166 (e.g., for storing demographic, psychographic, clinical,
environmental, social and behavioral tags at occurrences of health
events), a personal location data 168 (e.g., captured through GPS),
a location environment data 170 (e.g., captured through
weather/public channels), personal clinical data 172 (e.g., stored
suitably), and personal social data 174 (e.g., food, consumables).
In some embodiments, each element of encrypted PHOS/health N-grams
storage repository 154 may comprise a separate database; in other
embodiments, substantially all elements of encrypted PHOS/health
N-grams storage repository 154 may comprise a single database, with
appropriate fields and tags for ease of search and retrieval. Any
suitable database, table, array, list, texts, tags and other
storage mechanism may be used for encrypted PHOS/health N-Grams
storage repository 154 within the broad scope of the
embodiments.
[0079] Turning to FIG. 11, FIG. 11 is a simplified block diagram
illustrating example details of an embodiment of system 10. PHOS
API library 156 may include a personal signature encryption library
180, a personal health data services 182, a health n-grams
generator 184 (which can be the same or similar to n-gram generate
module 26 of FIG. 1), a health vector generator 186, a cloud
connector 188 and an open public health data connector library 190.
In various embodiments, cloud connector 188 may comprise a software
component embedded in, hosted on, or integrated with mobile device
12's networking functions to enable or enhance connectivity to
cloud 14. In an example embodiment, cloud connector 188 can allow
synchronization of data in mobile device 12 and cloud 14. Cloud
connector 188 can also allow access for applications 30 to PHOS
11's metadata (e.g., N-gram 28, health vectors and other such data)
through PHOS API 32, and enable its objects to be used
suitably.
[0080] Note that in this Specification, references to various
features (e.g., elements, structures, modules, components, steps,
operations, characteristics, etc.) included in "one embodiment",
"example embodiment", "an embodiment", "another embodiment", "some
embodiments", "various embodiments", "other embodiments",
"alternative embodiment", and the like are intended to mean that
any such features are included in one or more embodiments of the
present disclosure, but may or may not necessarily be combined in
the same embodiments.
[0081] In example implementations, at least some portions of the
activities outlined herein may be implemented in software in, for
example, PHOS 11. In some embodiments, one or more of these
features may be implemented in hardware, provided external to these
elements, or consolidated in any appropriate manner to achieve the
intended functionality. The various network elements may include
software (or reciprocating software) that can coordinate in order
to achieve the operations as outlined herein. In still other
embodiments, these elements may include any suitable algorithms,
hardware, software, components, modules, interfaces, or objects
that facilitate the operations thereof.
[0082] Furthermore, PHOS 11 described and shown herein (and/or its
associated structures) may also include suitable interfaces for
receiving, transmitting, and/or otherwise communicating data or
information in a network environment. Additionally, some of the
processors and memory elements associated with the various nodes
may be removed, or otherwise consolidated such that a single
processor and a single memory element are responsible for certain
activities. In a general sense, the arrangements depicted in the
FIGURES may be more logical in their representations, whereas a
physical architecture may include various permutations,
combinations, and/or hybrids of these elements. Moreover, if one
embodiment comprises elements A, B, and C, and a second embodiment
comprises elements B and D, then the inventive subject matter is
also considered to include other remaining combinations of A, B, C,
or D, even if not explicitly disclosed. It is imperative to note
that countless possible design configurations can be used to
achieve the operational objectives outlined here. Accordingly, the
associated infrastructure has a myriad of substitute arrangements,
design choices, device possibilities, hardware configurations,
software implementations, equipment options, etc.
[0083] As used herein, and unless the context dictates otherwise,
the term "coupled to" is intended to include both direct coupling
(in which two elements that are coupled to each other contact each
other) and indirect coupling (in which at least one additional
element is located between the two elements). Therefore, the terms
"coupled to" and "coupled with" are used synonymously.
[0084] In some of example embodiments, one or more memory elements
(e.g., memory element 24) can store data used for the operations
described herein. This includes the memory element being able to
store instructions (e.g., software, logic, code, etc.) in
non-transitory media such that the instructions are executed to
carry out the activities described in this Specification.
[0085] A processor can execute any type of instructions associated
with the data to achieve the operations detailed herein in this
Specification. In one example, processors (e.g., processor 25)
could transform an element or an article (e.g., data) from one
state or thing to another state or thing. In another example, the
activities outlined herein may be implemented with fixed logic or
programmable logic (e.g., software/computer instructions executed
by a processor) and the elements identified herein could be some
type of a programmable processor, programmable digital logic (e.g.,
a field programmable gate array (FPGA), an erasable programmable
read only memory (EPROM), an electrically erasable programmable
read only memory (EEPROM)), an ASIC that includes digital logic,
software, code, electronic instructions, flash memory, optical
disks, CD-ROMs, DVD ROMs, magnetic or optical cards, other types of
machine-readable mediums suitable for storing electronic
instructions, or any suitable combination thereof.
[0086] In operation, components in system 10 can include one or
more memory elements (e.g., memory element 24) for storing
information to be used in achieving operations as outlined herein.
These devices may further keep information in any suitable type of
non-transitory storage medium (e.g., random access memory (RAM),
read only memory (ROM), field programmable gate array (FPGA),
erasable programmable read only memory (EPROM), electrically
erasable programmable ROM (EEPROM), etc.), software, hardware, or
in any other suitable component, device, element, or object where
appropriate and based on particular needs.
[0087] The information being tracked, sent, received, or stored in
system 10 could be provided in any database, register, table,
cache, queue, control list, or storage structure, based on
particular needs and implementations, all of which could be
referenced in any suitable timeframe. Any of the memory items
discussed herein should be construed as being encompassed within
the broad term `memory element.` Similarly, any of the potential
processing elements, modules, and machines described in this
Specification should be construed as being encompassed within the
broad term `processor.`
[0088] It is also important to note that the operations and steps
described with reference to the preceding FIGURES illustrate only
some of the possible scenarios that may be executed by, or within,
the system. Some of these operations may be deleted or removed
where appropriate, or these steps may be modified or changed
considerably without departing from the scope of the discussed
concepts. In addition, the timing of these operations may be
altered considerably and still achieve the results taught in this
disclosure. The preceding operational flows have been offered for
purposes of example and discussion. Substantial flexibility is
provided by the system in that any suitable arrangements,
chronologies, configurations, and timing mechanisms may be provided
without departing from the teachings of the discussed concepts.
[0089] Note also that the disclosed subject matter herein enables
construction or configuration of a mobile device to operate on
digital data (e.g., raw sensor data, N-grams, etc.), beyond the
capabilities of a human or unconfigured (e.g., off-the-shelf)
mobile device. Although the digital data represents fitness or
health states, it should be appreciated that the digital data is a
representation of one or more digital models of fitness or health
states and not the actual fitness or health states themselves,
which comprise subjective experiences of a human being. By
instantiation of such digital models in the memory of the mobile
device, the mobile device is able to manage the digital models in a
manner that could provide utility to an individual (e.g., a user of
the mobile device) that the individual would lack without such a
tool.
[0090] It should be noted that any language directed to a computer
or computing device should be read to include any suitable
combination of computing devices, including servers, interfaces,
systems, databases, agents, peers, engines, controllers, modules,
or other types of computing devices operating individually or
collectively. One should appreciate the computing devices comprise
a processor configured to execute software instructions stored on a
tangible, non-transitory computer readable storage medium (e.g.,
hard drive, FPGA, PLA, solid state drive, RAM, flash, ROM, etc.).
The software instructions configure or program the computing device
to provide the roles, responsibilities, or other functionality as
discussed herein with respect to the disclosed apparatus and
operations. Further, the disclosed technologies can be embodied as
a computer program product that includes a non-transitory computer
readable medium storing the software instructions that causes a
processor to execute the disclosed steps associated with
implementations of computer-based algorithms, processes, methods,
or other instructions. In some embodiments, the various servers,
systems, databases, or interfaces exchange data using standardized
protocols or algorithms, possibly based on HTTP, HTTPS, AES,
public-private key exchanges, web service APIs, known financial
transaction protocols, or other electronic information exchanging
methods.
[0091] As used in the description herein and throughout the claims
that follow, when a system, engine, server, device, module, or
other computing element is described as configured to perform or
execute functions on data in a memory, the meaning of "configured
to" or "programmed to" refers to one or more processors or cores of
the computing element being programmed by a set of software
instructions stored in the memory of the computing element to
execute the set of functions on target data or data objects stored
in the memory.
[0092] One should appreciate that the disclosed techniques provide
many advantageous technical effects including reduction in latency
between a computing device ingesting healthcare data and generating
a prediction or recommendation. Latency is reduced through storage
of health care data in a memory and in the form of N-grams, which
can be computationally analyzed quickly.
[0093] Although the present disclosure has been described in detail
with reference to particular arrangements and configurations, these
example configurations and arrangements may be changed
significantly without departing from the scope of the present
disclosure. For example, although the present disclosure has been
described with reference to particular communication exchanges
involving certain network access and protocols, system 10 may be
applicable to other exchanges or routing protocols. Moreover,
although system 10 has been illustrated with reference to
particular elements and operations that facilitate the
communication process, these elements, and operations may be
replaced by any suitable architecture or process that achieves the
intended functionality of system 10.
[0094] Numerous other changes, substitutions, variations,
alterations, and modifications may be ascertained to one skilled in
the art and it is intended that the present disclosure encompass
all such changes, substitutions, variations, alterations, and
modifications as falling within the scope of the appended claims.
In order to assist the United States Patent and Trademark Office
(USPTO) and, additionally, any readers of any patent issued on this
application in interpreting the claims appended hereto, Applicant
wishes to note that the Applicant: (a) does not intend any of the
appended claims to invoke paragraph six (6) of 35 U.S.C. section
112 as it exists on the date of the filing hereof unless the words
"means for" or "step for" are specifically used in the particular
claims; and (b) does not intend, by any statement in the
specification, to limit this disclosure in any way that is not
otherwise reflected in the appended claims.
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