U.S. patent application number 17/826103 was filed with the patent office on 2022-09-15 for machine learning analysis of databases.
The applicant listed for this patent is Palantir Technologies Inc.. Invention is credited to Logan Kendall.
Application Number | 20220293256 17/826103 |
Document ID | / |
Family ID | 1000006362576 |
Filed Date | 2022-09-15 |
United States Patent
Application |
20220293256 |
Kind Code |
A1 |
Kendall; Logan |
September 15, 2022 |
MACHINE LEARNING ANALYSIS OF DATABASES
Abstract
Systems and methods include one or more processors, and a memory
storing instructions that, when executed by the one or more
processors, in conjunction with a particular machine learning model
for a subset of the instructions, cause the system to perform
automatically obtaining data of entities from databases based on a
frequency at which the data changes, storing the obtained data in a
repository, and using the particular machine learning model,
performing analysis within the databases.
Inventors: |
Kendall; Logan; (Seattle,
WA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Palantir Technologies Inc. |
Denver |
CO |
US |
|
|
Family ID: |
1000006362576 |
Appl. No.: |
17/826103 |
Filed: |
May 26, 2022 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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15824852 |
Nov 28, 2017 |
11373752 |
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17826103 |
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62438185 |
Dec 22, 2016 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G06Q 10/105 20130101;
G06Q 10/00 20130101; G06Q 10/10 20130101; G16H 10/60 20180101; G06Q
40/08 20130101; G16H 40/20 20180101; G16H 70/20 20180101 |
International
Class: |
G16H 40/20 20060101
G16H040/20; G16H 10/60 20060101 G16H010/60; G06Q 10/10 20060101
G06Q010/10; G16H 70/20 20060101 G16H070/20; G06Q 10/00 20060101
G06Q010/00; G06Q 40/08 20060101 G06Q040/08 |
Claims
1. A system comprising: one or more processors; and a memory
storing instructions that, when executed by the one or more
processors, in conjunction with a particular machine learning model
for a subset of the instructions, cause the system to perform:
obtaining data of entities from databases based on a frequency at
which the data changes; storing the obtained data in a repository;
using the particular machine learning model, detecting misuse among
entities, wherein training of the particular machine learning model
comprises: obtaining a first training dataset from among known
outcomes of previous analyses based on first sources verified to
have been associated with misuse; and obtaining a second training
dataset from among known outcomes of previous analyses based on
second sources verified to have been nonassociated with misuse; and
outputting an indication of the detected misuse.
2. The system of claim 1, wherein the obtaining of the first
training dataset and the second training dataset is further based
on a rate of convergence of the particular machine learning model
resulting from training using the first sources and the second
sources.
3. The system of claim 1, wherein the first sources are associated
with highest rates of convergence of the particular machine
learning model compared to other sources verified to have been
associated with misuse, and the second sources are associated with
highest rates of convergence of the particular machine learning
model compared to other sources verified to have been nonassociated
with misuse.
4. The system of claim 1, wherein the first sources are associated
with highest uncertainties compared to other sources verified to
have been associated with misuse, and the second sources are
associated with highest uncertainties compared to other sources
verified to have been nonassociated with misuse.
5. The system of claim 1, wherein the instructions further cause
the system to perform translating indicators of misuse within the
first training dataset and the second training dataset into
particular metrics, metric values, or weights, wherein the
particular metrics, metric values, or weights are used to
iteratively train the particular machine learning model.
6. The system of claim 5, wherein the iterative training comprises
modifying weights assigned to signals of the particular machine
learning model.
7. The system of claim 1, wherein the particular machine learning
model comprises a nearest neighbor model.
8. The system of claim 1, wherein the first training dataset and
the second training dataset are obtained from a different
model.
9. The system of claim 1, wherein the instructions further cause
the system to perform obtaining a third training dataset from among
previous analyses by selecting previous third sources that were
indeterminate regarding an association with misuse.
10. The system of claim 1, wherein the instructions further cause
the system to perform appending, to an interface, a natural
language explanation of the detected misuse and a correlation
between the detected misuse and a previous instance of misuse.
11. A method implemented by a computing system including one or
more processors and storage media storing machine-readable
instructions, wherein the method is performed using the one or more
processors, in conjunction with a particular machine learning
model, the method comprising: obtaining data of entities from
databases based on a frequency at which the data changes; storing
the obtained data in a repository; using the particular machine
learning model, detecting misuse among entities, wherein training
of the particular machine learning model comprises: obtaining a
first training dataset from among known outcomes of previous
analyses based on first sources verified to have been associated
with misuse; and obtaining a second training dataset from among
known outcomes of previous analyses based on second sources
verified to have been nonassociated with misuse; and outputting an
indication of the detected misuse.
12. The method of claim 11, wherein the obtaining of the first
training dataset and the second training dataset is further based
on a rate of convergence of the particular machine learning model
resulting from training using the first sources and the second
sources.
13. The method of claim 11, wherein the first sources are
associated with highest rates of convergence of the particular
machine learning model compared to other sources verified to have
been associated with misuse, and the second sources are associated
with highest rates of convergence of the particular machine
learning model compared to other sources verified to have been
nonassociated with misuse.
14. The method of claim 11, wherein the first sources are
associated with highest uncertainties compared to other sources
verified to have been associated with misuse, and the second
sources are associated with highest uncertainties compared to other
sources verified to have been nonassociated with misuse.
15. The method of claim 11, further comprising translating
indicators of misuse within the first training dataset and the
second training dataset into particular metrics, metric values, or
weights, wherein the particular metrics, metric values, or weights
are used to iteratively train the particular machine learning
model.
16. The method of claim 15, wherein the iterative training
comprises modifying weights assigned to signals of the particular
machine learning model.
17. The method of claim 11, wherein the particular machine learning
model comprises a nearest neighbor model.
18. The method of claim 11, wherein the first training dataset and
the second training dataset are obtained from a different
model.
19. The method of claim 11, further comprising obtaining a third
training dataset from among previous analyses by selecting previous
third sources that were indeterminate regarding an association with
misuse.
20. The method of claim 11, further comprising appending, to an
interface, a natural language explanation of the detected misuse
and a correlation between the detected misuse and a previous
instance of misuse.
Description
CROSS REFERENCE TO RELATED APPLICATIONS
[0001] This application is a continuation of U.S. application Ser.
No. 15/824,852, filed on Nov. 28, 2017, which claims the benefit
under 35 U.S.C. .sctn. 119(e) of U.S. Provisional Application Ser.
No. 62/438,185 filed Dec. 22, 2016, the content of which is
incorporated by reference in its entirety into the present
disclosure.
TECHNICAL FIELD
[0002] This disclosure relates to approaches for machine learning
analysis of databases.
BACKGROUND
[0003] There are various public and private benefit systems in the
society which are susceptible to misuse. Examples of such benefit
systems include healthcare, public housing, food assistance, social
security, senior services and community services. Certain
commercial programs, such as medical and dental insurance programs,
as well as auto, home and life insurance programs, are also subject
to misuse. Among these benefit systems, the healthcare system, both
public and private, is one or the most frequent targets for misuse
which results in substantial financial loss and potentially
substance abuse.
[0004] Under conventional approaches, a database may store
information relating to claims made for payment (e.g., medical
procedure claims, medical equipment claims, prescription claims,
doctor office claims, other benefit claims, etc.). Reviewing the
claims to identify potential misuse of the benefit system (e.g.,
fraudulent claims, prescription fraud, healthcare abuse/waste,
etc.) may be time consuming and very difficult. The amount of time
required and the difficulty of detecting potential frauds may lead
to inaccurate and/or incomplete misuse detection.
SUMMARY
[0005] Various embodiments of the present disclosure may include
systems, methods, and non-transitory computer readable media
configured to automatically detect misuse of a benefit system. A
database of claims may be analyzed to determine a healthcare
metric. The healthcare metric may be compared to a healthcare
threshold. Based on the comparison of the healthcare metric to the
healthcare threshold, a first lead for investigation may be
generated.
[0006] In some embodiments, the healthcare metric may characterize
a relationship between one or more pharmacy events and one or more
clinical events. In some embodiments, the healthcare metric may
characterize an amount of opiate doses received by a patient over a
period of time. In some embodiments, the healthcare metric may
characterize a billing pattern of one or more healthcare providers.
In some embodiments, the healthcare metric may be determined using
mutual entropy.
[0007] In some embodiments, the first lead may identify one or more
of patients, healthcare providers, and/or healthcare events.
[0008] In some embodiments, the systems, methods, and
non-transitory computer readable media are further configured to
generate a second lead for investigation based on the first lead.
In some embodiments, the second lead may identify one or more of
patients, healthcare providers, and/or healthcare events.
[0009] These and other features of the systems, methods, and
non-transitory computer readable media disclosed herein, as well as
the methods of operation and functions of the related elements of
structure and the combination of parts and economies of
manufacture, will become more apparent upon consideration of the
following description and the appended claims with reference to the
accompanying drawings, all of which form a part of this
specification, wherein like reference numerals designate
corresponding parts in the various figures. It is to be expressly
understood, however, that the drawings are for purposes of
illustration and description only and are not intended as a
definition of the limits of the invention.
BRIEF DESCRIPTION OF THE DRAWINGS
[0010] Certain features of various embodiments of the present
technology are set forth with particularity in the appended claims.
A better understanding of the features and advantages of the
technology will be obtained by reference to the following detailed
description that sets forth illustrative embodiments, in which the
principles of the invention are utilized, and the accompanying
drawings of which:
[0011] FIG. 1 illustrate an example environments for automatically
detecting misuse of a benefit system, in accordance with various
embodiments.
[0012] FIG. 2 illustrates an exemplary process for generating leads
based on pharmacy events and clinical events, in accordance with
various embodiments.
[0013] FIG. 3 illustrates an exemplary process for generating leads
based on opiate doses, in accordance with various embodiments.
[0014] FIG. 4 illustrates an exemplary process for generating leads
based on billing patterns, in accordance with various
embodiments.
[0015] FIG. 5 illustrates a flowchart of an example method, in
accordance with various embodiments.
[0016] FIG. 6 illustrates a block diagram of an example computer
system in which any of the embodiments described herein may be
implemented.
DETAILED DESCRIPTION
[0017] A claimed solution rooted in computer technology overcomes
problems specifically arising in the realm of computer technology.
In various embodiments, a computing system is configured to access
and analyze a database of claims to determine leads for benefit
misuse investigations. For example, in various embodiments, a
computing system is configured to access and analyze a plurality of
claims to determine leads for healthcare fraud investigations.
Information relating to healthcare provisions (e.g., patient
information, procedure performed, medical equipment used,
prescription provided/filled, provider information, etc.) may be
parsed from claims data to determine patterns for patients and/or
healthcare providers. The patterns may be analyzed to determine
specific patterns indicative of fraud/health case misuse and
particular patients/healthcare providers/healthcare events may be
tagged as a lead for investigation. For example, combinations of
pharmacy events and clinical events may be analyzed to determine
whether expected pharmacy events/clinical events are occurring. If
the expected pharmacy events/clinical events are not occurring, the
pharmacy events/the clinical events/patients/healthcare providers
may be tagged as potential leads for healthcare misuse. As another
example, the amount of opiate doses within filled prescription may
be tracked to determine whether a patient fits the profile of a
drug seeker and the patient/healthcare provider may be tagged as
potential leads. As another example, a doctor's billing pattern may
be analyzed to determine likelihood of billing fraud (e.g.,
upcoding) and the doctor may be tagged as a potential lead. The
patterns may be analyzed using mutual entropy. Detected leads may
be used to determine other leads. For example, a lead for a drug
seeker may be used to track which doctors have provided
prescription for the drug seeker, which may be used to determine
leads for doctors engaged in prescription misuse.
[0018] The number of healthcare claims (e.g., medical procedure
claims, hospital claims, medical equipment claims, prescription
claims, doctor office claims, etc.) is in the range of the millions
or billions per year. Individual healthcare claims may include
numerous types of data, such as billing codes (e.g., procedure
code, diagnosis code, etc.), patient identifier, location, service
provider identifier, service date, and the like. Because databases
of medical claims may contain vast amount of information,
selectively mining the available information for useful purposes,
such as to identify leads to potential fraudulent claims, is not a
trivial task. The present disclosure enables automatic detection of
misuse of a healthcare system. The techniques described herein
enable automatic tagging of healthcare events, patients, and/or
healthcare providers as leads for investigation.
[0019] Healthcare waste, fraud and/or abuse may be examples of
healthcare misuse. As used herein, fraud refers to a scheme or
artifice to defraud any healthcare program or entity or to obtain
any of the money or property owned by, or under the custody or
control of, any healthcare program or entity. Waste refers to the
overutilization of services or other practices that, directly or
indirectly, result in unnecessary costs to the healthcare system.
Abuse refers to any action that may, directly or indirectly, result
in one or more of unnecessary costs to the healthcare system,
improper payment for services, payment for services that fail to
meet professionally recognized standards of care, and/or services
that are medically unnecessary.
[0020] While the disclosure is described herein with respect to
fraud and fraud lead detection, this is merely for illustrative
purposes and is not meant to be limiting. For example, the
techniques described herein may apply to waste lead detection
and/or abuse lead detection. The techniques described herein may
apply to lead detection for misuse of any type of benefit systems
which provide for payment/reimbursement to individuals and/or
organizations for services performed/received and/or equipment
provided/received.
[0021] FIG. 1 illustrates an example environment 100 for
automatically detecting misuse of a benefit system, in accordance
with various embodiments. The example environment 100 may include a
computing system 102 and a database 104. The database 104 may
include a database and/or a system of databases that receive and
store data related to healthcare claims (e.g., medical procedure
claims, hospital claims, medical equipment claims, prescription
claims, doctor office claims, etc.) submitted by individuals and/or
organizations. The data may be organized based on individuals
receiving healthcare services/products, individuals/organizations
providing healthcare services/products, time (e.g., a particular
duration of time), insurance providers, and/or other information.
Individuals receiving healthcare services/products may be referred
to as patients. Individuals/organizations providing healthcare
services/products may be referred to as healthcare providers.
Healthcare providers may include facilities, institutions, and/or
groups, such as hospitals and clinics, and/or individual
practitioners such as doctors, dentists, nurses, pharmacists, and
therapists. The database 104 may include supplemental information
about the healthcare claims, such as individual/organization
contact information, medical code information, and/or other
information.
[0022] Although the database 104 is shown in FIG. 1 as a single
entity, this is merely for ease of reference and is not limiting.
The database 104 may represent one or more databases and/or one or
more storage devices storing databases located in the same or
different locations. The database 104 may be located in the same
and/or different locations from the computing system 102. For
example, the database 104 may be stored within the memory of the
computing system 102 and/or a memory coupled to the computing
system 102. The database 104 may exchange information with the
computing system 102 via one or more networks.
[0023] The computing system 102 may include one or more processors
and memory. The processor(s) may be configured to perform various
operations by interpreting machine-readable instructions stored in
the memory. As shown in FIG. 1, in various embodiments, the
computing system 102 may include a claims engine 112, a metric
engine 114, and a lead engine 116, and/or other engines. The metric
engine 114 may include an events engine 122, a doses engine 124,
and a billing engine 126. The metric engine 114 may be executed by
the processor(s) of the computing system 102 to perform various
operations including those described in reference to the events
engine 122, the doses engine 124, and the billing engine 126. The
environment may include a data store (not shown) that is accessible
to the computing system 102. In some embodiments, the data store
may include various databases, software packages, and/or other data
that are available for download, installation, and/or
execution.
[0024] In various embodiments, the claims engine 112 may be
configured to access and analyze one or more databases of claims.
For example, the claims engine 112 may access and analyze
healthcare claims stored in the database 104. In various
embodiments, the claims engine 112 may parse the information
contained within the healthcare claims to identify relevant
information for analysis. In some embodiments, the database 104 may
include information from healthcare claims which are formatted for
access by the claims engine 112. In some embodiments, the database
104 may include healthcare claims and the claims engine 112 may
provide one or more of clean-up, enrichment, and/or transformation
of the information within the healthcare claims for analysis. For
example, the claims engine 112 may identify paid vs unpaid claims,
trim unnecessary data, incorporate data from other sources (e.g.,
patient information, healthcare provider information, etc.) that
provides context for the healthcare claims, remove duplicative
information within the healthcare claims, and/or other
transformation to allow the information contained within the
healthcare claims to be used for misuse lead detection.
[0025] In some embodiments, the claims engine 112 may be configured
to access and consolidate information contained in multiple
databases of claims. The claims engine 122 may access and extract
different information from different databases of claims for
analysis. For example, the claims engine 112 may collect claims
information from databases of claims from healthcare providers,
databases of claims from insurance companies, databases of claims
from publicly available information, and/or other databases.
[0026] In some embodiments, the claims engine 112 may incorporate
the information obtained with the databases of claims and/or other
sources (e.g., external sources) into one or more object types
defined by one or more ontologies. For example, the claims engine
112 may create from the healthcare claims stored in the database
104 different objects corresponding to different healthcare
participants and/or events, such as healthcare provider objects,
patient objects, healthcare event objects, service objects,
equipment objects, prescription objects, billing objects, and/or
other objects. Packaging of information into objects may enable
selective access and/or modification of the information contained
within the objects. Information packaged within the objects may be
accessed and/or modified during misuse lead detection.
[0027] In various embodiments, the events engine 122 may be
configured determine one or more healthcare metrics based on the
analysis of one or more databases of claims. The healthcare metric
determined by the events engine 122 may characterize a relationship
between one or more pharmacy events and one or more clinical
events. A pharmacy event may refer to a medical event in which a
drug is prescribed and/or a prescription for a drug is filled. For
example, a pharmacy event may refer to a doctor providing a drug
prescription to a patient and/or a pharmacy providing the drug to
the patient. A clinical event may refer to a medical event in which
a healthcare provider may assess a patient's need for
pharmaceutical treatment. For example, a clinical event may include
a visit to a doctor's office for a procedure that is typically
accompanied by one or more prescriptions for drugs. As another
example, a clinical event may include a check-up visit in which a
patient's health is examined to determine whether a new
prescription is required and/or a previous prescription needs to be
reissued/refilled.
[0028] The healthcare metric may characterize whether one or more
pharmacy events are accompanied by one or more expected clinical
events. For example, certain types of drugs (e.g., Schedule II
drugs) may require a patient to receive a new prescription to get a
refill. Based on the analysis of database(s) of claims, the events
engine 122 may determine a healthcare metric that characterizes
whether a patient's filling of drugs are preceded by clinical
events in which a healthcare provider would have assessed the
patient's need for the drugs and provided the new prescription. For
example, for a particular healthcare provider and/or a particular
patient, the events engine 122 may count the number of pharmacy
events (e.g., a pharmacy fills a drug prescription written by the
healthcare provider) and the number of clinical events that may be
associated with the pharmacy events. The events engine 122 may
count the number of clinical events that occur within a certain
period of time before and/or after the pharmacy event.
[0029] A healthcare provider whose practice includes an expected
number of clinical events associated with pharmacy events may be
scored with a satisfactory healthcare metric (e.g., high or low
score). A healthcare provider whose practice includes a lower than
expected number of clinical events associated with pharmacy events
may be scored with an unsatisfactory healthcare metric (e.g., low
or high score). A healthcare provider whose practice includes a
lower than expected number of clinical events associated with
pharmacy events may be practicing poor standard of care (e.g., bad
pain management--not meeting with patients to whom the healthcare
provider is providing prescriptions). A healthcare provider whose
clinical events are not occurring within a certain time duration of
the pharmacy events may be practicing poor standard of care.
[0030] A patient who is associated with an expected number of
clinical events for pharmacy events may be scored with a
satisfactory healthcare metric (e.g., high or low score). A patient
who is associated with a lower than expected number of clinical
events for pharmacy events may be scored with an unsatisfactory
healthcare metric (e.g., low or high score). A patient who is
associated with a lower than expected number of clinical events for
pharmacy events may have a healthcare provider practicing bad pain
management (e.g., not meeting with patients to whom the healthcare
provider is providing prescriptions) and/or may be falsifying
prescriptions.
[0031] In various embodiments, the doses engine 124 may be
configured to determine one or more healthcare metrics based on the
analysis of one or more databases of claims. The healthcare metric
determined by the doses engine 124 may characterize the amount of
opiate doses received by a patient over one or more periods of
time. The doses engine 124 may leverage information within the
database of claims to determine the amount of opiate doses received
by a patent. The doses engine 124 may convert the amount of opiate
doses received by the patient into a morphine equivalent. For
example, different types of drugs may be associated with different
levels of morphine, and information about the types and amounts of
drugs received by a patient may be converted into a morphine
equivalent. The healthcare metric may characterize the amount of
opiate doses received by a patient based on the morphine equivalent
received by the patient. In some embodiment, the opiate doses may
be aggregated on a periodic basis (e.g., daily, weekly, monthly,
yearly, etc.). High amounts of morphine equivalent for a patient
for a given period of time may indicate that the patient may be a
drug seeker. In some embodiments, the healthcare metric for the
individual patients may be aggregated to determine the healthcare
metric for a healthcare provider. High score for a healthcare
provider may indicate that the healthcare provider potentially
provides prescriptions for one or more drug seeking patients.
[0032] In some embodiments, the healthcare metric determined by the
doses engine 124 may be adjusted based on patient information. For
example, the healthcare metric may be adjusted based on the size of
the patient so that the variance of amount of opiate doses received
by the patient based on the size of the patient is factored into
the healthcare metric. As another example, the healthcare metric
may be adjusted based on the patient's current health condition
(e.g., diagnosed diseases) so that the variance of amount of opiate
doses received by the patient due to the patient's health condition
is factored into the healthcare metric. For instance, a patient
diagnosed with cancer may be expected to receive higher amounts of
opiate doses than a patient diagnosed with a cold.
[0033] In various embodiments, the billing engine 126 may be
configured to determine one or more healthcare metrics based on the
analysis of one or more databases of claims. The healthcare metric
determined by the billing engine 126 may be characterized by a
billing pattern of one or more healthcare providers. The healthcare
metric determined by the billing engine 126 may indicate whether
the healthcare providers may be engaged in fraudulent billing
practices. For example, the healthcare metric may indicate whether
the healthcare providers are engaged in upcoding (e.g., using a
more expensive code for payment) and/or other practices to receive
money for medical services/equipment that was not provided to
patients.
[0034] In some embodiments, the billing engine 126 may use mutual
entropy to determine whether the healthcare providers are engaging
in fraudulent billing practices. The billing engine 126 may use
mutual entropy to determine mutual information between the
healthcare providers' billings and the patients seen by the
healthcare providers. The mutual information may indicate whether
the healthcare providers' billings (indicating the medical services
performed and/or medical equipment used/provided, etc.) are
independent or dependent on the patients seen by the healthcare
providers. Mutual entropy may determine whether there is a
connection between a particular patient/visit and the treatment
provided/billed by the healthcare provider. Mutual entropy may
determine whether there is a connection between a patient and the
type of treatment received by the patient. Healthcare providers
that bill for the same/similar types of treatment regardless of the
patient identity/visit may be engaged in fraudulent billing
practices. Healthcare providers with billing entries that are
tailored to different patients/visits may receive a higher
healthcare metric than healthcare providers with billing entries
that include same/similar claims across different patients/visits.
For example, a doctor who bills for a urine test for every patient
may be scored with a lower healthcare metric than a doctor who
bills a urine test for a subgroup of patients. A healthcare
provider with a low healthcare metric determined based on mutual
entropy may be engaged in "cookie-cutter billing," where the
healthcare providers bills for the same/similar treatment
regardless of the patient they see and/or the type of visit.
[0035] Calculation of healthcare metric based on mutual entropy may
be grouped by specialty/classes of healthcare providers. For
example, a certain specialty (e.g., radiologists) may use a smaller
subset of codes for billing than another specialty (e.g., family
medicine). Calculating mutual entropy across different specialties
may result in healthcare providers in specialties with smaller
number of billing codes having lower healthcare metrics than
healthcare provider in specialties with larger number of billing
codes. Grouping the calculation of mutual entropy by
specialty/classes of healthcare providers may enable comparison of
billing practices among similar types of healthcare providers.
[0036] In some embodiments, the billing engine 126 may weigh
different parameters for the mutual entropy calculation
differently. For example, the billing engine 126 may focus on
(weigh more heavily) certain kinds of treatments, equipment, and/or
specialties that are more prone to being subject of fraudulent
billing practices. As another example, the billing engine 126 may
discard (weigh less heavily) most common codes used by healthcare
providers (e.g., codes expected to be billed for every/most
patients). Such codes may be of such high volume that they may skew
the mutual entropy calculation and hide misuse of less common
codes. Disregarding such codes may allow the mutual entropy
calculation (and the healthcare metric) to reflect the misuse of
less common codes.
[0037] In some embodiments, the billing engine 126 may determine
mutual entropy by different time periods (e.g., per visit, per a
number of visits, daily, weekly, monthly, yearly, etc.), by the
healthcare provider's specialty, and/or by specific codes (e.g.,
CPT code) for different patients/healthcare providers. For example,
determining mutual entropy based on treatments provided to patients
over a three month period may provide different indication of the
healthcare provider's billing practices than determining mutual
entropy based on treatments provided to patients over a single
visit.
[0038] In some embodiments, the billing engine 126 may use billing
trends to determine whether the healthcare providers are engaging
in fraudulent billing practices. The billing engine 126 may analyze
the billing entries of healthcare providers to determine if the
healthcare providers are increasing the use of more expensive
billing codes over time (e.g., the healthcare providers are
starting to upcode, etc.). The billing engine 126 may analyze the
billing entries to detect patterns of billing independent of
patients seen by the healthcare providers. For example, the
healthcare providers may bill one or more particular codes at a
regular interval (e.g., a healthcare provider may bill a particular
code every thirty days regardless of the identities and/or the
number of patients seen by the healthcare provider). The healthcare
metric may characterize the pattern detected by the billing engine
126.
[0039] The billing engine 126 may analyze the billing entries to
determine levels and/or periodicity of billing that are independent
of external factors. For example, healthcare providers may, on
average, experience fluctuations on the number of patients
seen/number of billing entries/types of billing entries based on
the time of the year and/or weather conditions. The billing engine
126 may analyze the billing entries to determine whether particular
healthcare providers do not experience fluctuations in billing
experienced by other healthcare providers (e.g., a particular
healthcare provider's billing is not affected by changes in
weather, temperature, humidity, etc.).
[0040] In some embodiments, the billing engine 126 may access
information about external factors to identify periods of time when
the billings of the healthcare providers may fluctuate. For
example, the billing engine 126 may access weather information to
determine periods when healthcare providers' billings are expected
to decrease. The billing engine 126 may analyze the billing entries
to determine which healthcare providers' billing entries stayed
level or increased during those periods.
[0041] In some embodiments, the billing engine 126 may analyze
billing entries to determine when the providers and/or patients are
engaging in unlikely/impossible activities. For example, the
billing engine 126 may analyze billing entries to determine when a
particular provider has billed more than 24 hours' worth of codes
during a single day (billing an "impossible day"). As another
example, the billing engine 126 may analyze billing entries to
determine when a particular patient's multiple visits to a
healthcare provider and/or visits to multiple healthcare providers
in a set amount of time may be of suspect. For example, a
particular patient may have visited more healthcare providers/had
more visits to a healthcare provider than would be likely during a
given time period (e.g., a day). As another example, a particular
patient may have visited healthcare providers located far from each
other such that the timing of the visit (e.g., visited during the
same day) is unlikely. Such unlikely visits by a patient may
indicate that one or more healthcare providers are engaging in
medical identify theft (using patient information to bill for
patients not seen).
[0042] In some embodiments, one or more healthcare metrics may be
determined using the systems/methods/non-transitory computer
readable medium as disclosed in application Ser. No. 15/181,712,
"FRAUD LEAD DETECTION SYSTEM FOR EFFICIENTLY PROCESSING
DATABASE-STORED DATA AND AUTOMATICALLY GENERATING NATURAL LANGUAGE
EXPLANATORY INFORMATION OF SYSTEM RESULTS FOR DISPLAY IN
INTERACTIVE USER INTERFACES," filed on Jun. 14, 2016, which is
hereby incorporated by reference in its entirety.
[0043] In various embodiments, the lead engine 116 may be
configured to compare a healthcare metric to a healthcare threshold
and generate one or more leads for investigation based on the
comparison. A healthcare threshold may include a static value
and/or a dynamic value to which the healthcare metric may be
compared. A healthcare threshold may be set manually (e.g., by a
user) and/or may be set automatically. For example, for different
types of healthcare metrics (e.g., determined based on pharmacy
events and clinical events, opiate doses, billing pattern, mutual
entropy, etc.), a user may manually set the healthcare threshold
such that healthcare metrics that meet, exceed, or fall below the
healthcare threshold are used to generate leads for investigation.
As another example, for different types of healthcare metrics, the
lead engine 116 may determine the healthcare threshold based on
aggregation of healthcare metrics such that the healthcare
threshold represents a certain statistical deviation from the
aggregated healthcare metrics.
[0044] For example, for healthcare metrics of healthcare providers
and/or patients determined based on pharmacy events and clinical
events, the healthcare threshold may be determined based on an
expected numbers of pharmacy events associated with clinical events
(e.g., ratio of numbers of pharmacy events to numbers of associated
clinical events) and/or based on the duration between the
occurrences of pharmacy events and associated clinical events
(e.g., does a clinical event occur within a certain duration before
and/or after a pharmacy event; how many clinical events occur
within a certain duration before and/or after pharmacy events).
Based on the healthcare metric of the healthcare providers and/or
the patients not satisfying the healthcare threshold, the lead
engine 116 may identify as leads for investigation one or more of
the corresponding healthcare providers, the patients, and/or the
healthcare events (e.g., clinical event, pharmacy event, etc.).
[0045] As another example, for healthcare metrics of healthcare
providers and/or patients determined based on opiate doses received
by patients, the healthcare threshold may be determined based on a
certain amount of opiate doses/morphine equivalent. Based on the
healthcare metric of the healthcare providers and/or the patients
not satisfying the healthcare threshold, the lead engine 116 may
identify as leads for investigation one or more of the
corresponding healthcare providers, the patients, and/or the
healthcare events (e.g., clinical event, pharmacy event, etc.).
[0046] As another example, for healthcare metrics of healthcare
providers determined based on mutual entropy, the healthcare
threshold may be determined based on a value indicating a certain
dependence/independence between the healthcare providers' billings
and the patients seen by the healthcare providers. Based on the
healthcare metric of the healthcare providers not satisfying the
healthcare threshold, the lead engine 116 may identify as leads for
investigation one or more of the corresponding healthcare
providers.
[0047] As another example, for healthcare metrics of healthcare
providers determined based on billing trends, the healthcare
threshold may be determined based on a value indicating a certain
trend/pattern of billing. For example, the healthcare threshold may
be set based on a maximum amount of billings and/or increase in the
use of more expensive billing codes for a set duration of time,
based on the level/periodicity of billings that are independent of
external factors, and/or based on the number/level of
unlikely/impossible activities reflected by the claims. Based on
the healthcare metric of the healthcare providers not satisfying
the healthcare threshold, the lead engine 116 may identify as leads
for investigation one or more of the corresponding healthcare
providers.
[0048] In some embodiments, the lead engine 116 may use the
comparison of healthcare metrics to healthcare thresholds as one
among multiple factors for generating leads for investigation. The
lead engine 116 may review other factors when a healthcare metric
does not satisfy the healthcare threshold. For example, with
respect to healthcare metrics of healthcare providers and/or
patients determined based on opiate doses received by patients,
other factors may include whether a patient has a recorded history
of displaying characteristics/behaviors of a drug dependent person
(e.g., frequent visits to the emergency room, seeing many different
doctors, visiting many different pharmacies, etc.). The lead engine
116 may use a classifier to return a probability, based on the
comparison of the healthcare metric to healthcare thresholds and
other factors, that a patient is a drug seeker.
[0049] As another example, with respect to healthcare metrics of
healthcare providers determined based on mutual entropy, other
factors may include total billing and/or total volume of
services/products provided by the healthcare providers. The lead
engine 116 may identify as a lead one or more healthcare providers
whose healthcare metric does not satisfy the healthcare threshold
and whose totally billing/volume is higher than others (e.g., top
bills, top volumes).
[0050] In some embodiments, the lead engine 116 may use multiple
comparisons between healthcare metrics and healthcare thresholds to
generate leads for investigation. For example, the lead engine 116
may identify patients whose healthcare metrics (e.g., determined
based on amount of opiate doses and/or other factors) indicate that
the patients may be drug seekers and identify healthcare providers
whose healthcare metrics (e.g., determined based on clinical events
and pharmacy events) indicate the healthcare providers may be
practicing bad pain management. The overlap between the identified
patients and the identified healthcare providers (including
healthcare providers potentially practicing bad pain management and
seeing potentially drug seeking patients) may be identified as
leads for investigation.
[0051] In various embodiments, the lead engine 116 is configured to
generate additional leads for investigation based on previously
generated leads. For example, based on a lead identifying
healthcare providers providing unnecessary amounts of drugs, the
lead engine 116 may generate leads for investigations patients who
see the identified healthcare providers. The patients who were
prescribed higher amounts opiate doses and/or displaying
characteristics/behaviors of drug seeker may be identified as
leads. As another example, a lead identifying a patient as a
potential drug seeker may be used to track which healthcare
providers have provided prescription for the patient. A healthcare
providers who see and/or provide prescriptions for more patients
identified as potential drug seeker may be identified as leads.
Backtracking leads of healthcare providers may enable construction
of a network model that provides a view of how tightly connected
the healthcare providers are through their patients. For example,
the network model may indicate which healthcare providers may be
connected in their misuse of the healthcare system and/or may
indicate which healthcare providers a drug seeking patient may turn
to if one of the identified healthcare providers is shut down.
[0052] In some embodiments, one or more healthcare thresholds may
be determined, and/or one or more leads may be identified and/or
reported using the systems/methods/non-transitory computer readable
medium as disclosed in application Ser. No. 15/181,712, "FRAUD LEAD
DETECTION SYSTEM FOR EFFICIENTLY PROCESSING DATABASE-STORED DATA
AND AUTOMATICALLY GENERATING NATURAL LANGUAGE EXPLANATORY
INFORMATION OF SYSTEM RESULTS FOR DISPLAY IN INTERACTIVE USER
INTERFACES," filed on Jun. 14, 2016, incorporated supra.
[0053] FIG. 2 illustrates an exemplary process 200 for generating
leads based on pharmacy events and clinical event. The process 200
may be implemented in various environments, including, for example,
the environment of FIG. 1. At block 202, one or more pharmacy
events for a patient/healthcare provider may be identified from one
or more databases of claims. At block 204, one or more clinical
events for a patient/healthcare provider may be identified from one
or more databases of claims. At block 206, one or more matches
between the pharmacy event(s) and the clinical event(s) may be
determined. A match between the pharmacy event(s) and the clinical
event(s) may exist when the timing of the pharmacy event(s) and the
clinical event(s) indicate that the pharmacy event(s) occurred as a
result of the clinical event(s). At block 208, one or more leads
may be generated based on the matches between the pharmacy event(s)
and the clinical event(s). Lead(s) may be generated when the
matches between the pharmacy event(s) and the clinical event(s)
indicate less than a desired number/timing of clinical events for
pharmacy events (e.g., a patient getting prescriptions filled
without seeing a doctor, a doctor writing prescriptions for a
patient without seeing the patients, etc.).
[0054] FIG. 3 illustrates an exemplary process 300 for generating
leads based on opiate doses. The process 300 may be implemented in
various environments, including, for example, the environment of
FIG. 1. At block 302, the dose amounts for a patient may be
determined. At block 304, the morphine equivalent of the dose
amounts may be determined. At block 306, one or more drug seeking
behaviors/characteristics of the patient may be determined. At
block 308, the probability that the patient is a drug seeker may be
determined based on the morphine equivalent of the dose amounts and
the drug seeking behaviors/characteristics of the patients. At
block 310, identifies of one or more potentially drug-seeking
patients may be used to backtrack to the healthcare providers who
provided prescription to these patients. At block 312, one or more
leads may be generated based on the identified patients and/or
identified healthcare providers.
[0055] FIG. 4 illustrates an exemplary process 400 for generating
leads based on billing patterns. The process 400 may be implemented
in various environments, including, for example, the environment of
FIG. 1. At block 402, billing patterns of one or more healthcare
providers may be determined. At block 404, potentially fraudulent
billing patterns may be identified. In some embodiments,
identifying potentially fraudulent billing patterns may include the
use of mutual entropy 404A to determine the dependence/independence
between the healthcare providers' billings and the patients seen by
the healthcare provider. In some embodiments, identifying
potentially fraudulent billing patterns may include the detection
of trends 404B that indicate upcoding. In some embodiments,
identifying potentially fraudulent billing patterns may include
determining the influence/lack of influence of external factors
404C on billing patterns. In some embodiments, identifying
potentially fraudulent billing patterns may include detecting
unlikely/impossible activities 404D. At block 406, one or more
leads may be generated based on the potentially fraudulent billing
patterns.
[0056] FIG. 5 illustrates a flowchart of an example method 500,
according to various embodiments of the present disclosure. The
method 500 may be implemented in various environments including,
for example, the environment 100 of FIG. 1. The operations of
method 500 presented below are intended to be illustrative.
Depending on the implementation, the example method 500 may include
additional, fewer, or alternative steps performed in various orders
or in parallel. The example method 500 may be implemented in
various computing systems or devices including one or more
processors.
[0057] At block 502, a database of claims may be analyzed. At block
504, a healthcare metric may be determined based on the analyses of
the database of claims. At block 506, the healthcare metric may be
compared to a healthcare threshold. At block 508, based on the
comparison of the healthcare metric to the healthcare threshold, a
first lead for investigation may be generated.
Hardware Implementation
[0058] The techniques described herein are implemented by one or
more special-purpose computing devices. The special-purpose
computing devices may be hard-wired to perform the techniques, or
may include circuitry or digital electronic devices such as one or
more application-specific integrated circuits (ASICs) or field
programmable gate arrays (FPGAs) that are persistently programmed
to perform the techniques, or may include one or more hardware
processors programmed to perform the techniques pursuant to program
instructions in firmware, memory, other storage, or a combination.
Such special-purpose computing devices may also combine custom
hard-wired logic, ASICs, or FPGAs with custom programming to
accomplish the techniques. The special-purpose computing devices
may be desktop computer systems, server computer systems, portable
computer systems, handheld devices, networking devices or any other
device or combination of devices that incorporate hard-wired and/or
program logic to implement the techniques.
[0059] Computing device(s) are generally controlled and coordinated
by operating system software, such as iOS, Android, Chrome OS,
Windows XP, Windows Vista, Windows 7, Windows 8, Windows Server,
Windows CE, Unix, Linux, SunOS, Solaris, iOS, Blackberry OS,
VxWorks, or other compatible operating systems. In other
embodiments, the computing device may be controlled by a
proprietary operating system. Conventional operating systems
control and schedule computer processes for execution, perform
memory management, provide file system, networking, I/O services,
and provide a user interface functionality, such as a graphical
user interface ("GUI"), among other things.
[0060] FIG. 6 is a block diagram that illustrates a computer system
600 upon which any of the embodiments described herein may be
implemented. The computer system 600 includes a bus 602 or other
communication mechanism for communicating information, one or more
hardware processors 604 coupled with bus 602 for processing
information. Hardware processor(s) 604 may be, for example, one or
more general purpose microprocessors.
[0061] The computer system 600 also includes a main memory 606,
such as a random access memory (RAM), cache and/or other dynamic
storage devices, coupled to bus 602 for storing information and
instructions to be executed by processor 604. Main memory 606 also
may be used for storing temporary variables or other intermediate
information during execution of instructions to be executed by
processor 604. Such instructions, when stored in storage media
accessible to processor 604, render computer system 600 into a
special-purpose machine that is customized to perform the
operations specified in the instructions.
[0062] The computer system 600 further includes a read only memory
(ROM) 608 or other static storage device coupled to bus 602 for
storing static information and instructions for processor 604. A
storage device 610, such as a magnetic disk, optical disk, or USB
thumb drive (Flash drive), etc., is provided and coupled to bus 602
for storing information and instructions.
[0063] The computer system 600 may be coupled via bus 602 to a
display 612, such as a cathode ray tube (CRT) or LCD display (or
touch screen), for displaying information to a computer user. An
input device 614, including alphanumeric and other keys, is coupled
to bus 602 for communicating information and command selections to
processor 604. Another type of user input device is cursor control
616, such as a mouse, a trackball, or cursor direction keys for
communicating direction information and command selections to
processor 604 and for controlling cursor movement on display 612.
This input device typically has two degrees of freedom in two axes,
a first axis (e.g., x) and a second axis (e.g., y), that allows the
device to specify positions in a plane. In some embodiments, the
same direction information and command selections as cursor control
may be implemented via receiving touches on a touch screen without
a cursor.
[0064] The computing system 600 may include a user interface module
to implement a GUI that may be stored in a mass storage device as
executable software codes that are executed by the computing
device(s). This and other modules may include, by way of example,
components, such as software components, object-oriented software
components, class components and task components, processes,
functions, attributes, procedures, subroutines, segments of program
code, drivers, firmware, microcode, circuitry, data, databases,
data structures, tables, arrays, and variables.
[0065] In general, the word "module," as used herein, refers to
logic embodied in hardware or firmware, or to a collection of
software instructions, possibly having entry and exit points,
written in a programming language, such as, for example, Java, C or
C++. A software module may be compiled and linked into an
executable program, installed in a dynamic link library, or may be
written in an interpreted programming language such as, for
example, BASIC, Perl, or Python. It will be appreciated that
software modules may be callable from other modules or from
themselves, and/or may be invoked in response to detected events or
interrupts. Software modules configured for execution on computing
devices may be provided on a computer readable medium, such as a
compact disc, digital video disc, flash drive, magnetic disc, or
any other tangible medium, or as a digital download (and may be
originally stored in a compressed or installable format that
requires installation, decompression or decryption prior to
execution). Such software code may be stored, partially or fully,
on a memory device of the executing computing device, for execution
by the computing device. Software instructions may be embedded in
firmware, such as an EPROM. It will be further appreciated that
hardware modules may be comprised of connected logic units, such as
gates and flip-flops, and/or may be comprised of programmable
units, such as programmable gate arrays or processors. The modules
or computing device functionality described herein are preferably
implemented as software modules, but may be represented in hardware
or firmware. Generally, the modules described herein refer to
logical modules that may be combined with other modules or divided
into sub-modules despite their physical organization or
storage.
[0066] The computer system 600 may implement the techniques
described herein using customized hard-wired logic, one or more
ASICs or FPGAs, firmware and/or program logic which in combination
with the computer system causes or programs computer system 600 to
be a special-purpose machine. According to one embodiment, the
techniques herein are performed by computer system 600 in response
to processor(s) 604 executing one or more sequences of one or more
instructions contained in main memory 606. Such instructions may be
read into main memory 606 from another storage medium, such as
storage device 610. Execution of the sequences of instructions
contained in main memory 606 causes processor(s) 604 to perform the
process steps described herein. In alternative embodiments,
hard-wired circuitry may be used in place of or in combination with
software instructions.
[0067] The term "non-transitory media," and similar terms, as used
herein refers to any media that store data and/or instructions that
cause a machine to operate in a specific fashion. Such
non-transitory media may comprise non-volatile media and/or
volatile media. Non-volatile media includes, for example, optical
or magnetic disks, such as storage device 610. Volatile media
includes dynamic memory, such as main memory 606. Common forms of
non-transitory media include, for example, a floppy disk, a
flexible disk, hard disk, solid state drive, magnetic tape, or any
other magnetic data storage medium, a CD-ROM, any other optical
data storage medium, any physical medium with patterns of holes, a
RAM, a PROM, and EPROM, a FLASH-EPROM, NVRAM, any other memory chip
or cartridge, and networked versions of the same.
[0068] Non-transitory media is distinct from but may be used in
conjunction with transmission media. Transmission media
participates in transferring information between non-transitory
media. For example, transmission media includes coaxial cables,
copper wire and fiber optics, including the wires that comprise bus
602. Transmission media can also take the form of acoustic or light
waves, such as those generated during radio-wave and infra-red data
communications.
[0069] Various forms of media may be involved in carrying one or
more sequences of one or more instructions to processor 604 for
execution. For example, the instructions may initially be carried
on a magnetic disk or solid state drive of a remote computer. The
remote computer can load the instructions into its dynamic memory
and send the instructions over a telephone line using a modem. A
modem local to computer system 600 can receive the data on the
telephone line and use an infra-red transmitter to convert the data
to an infra-red signal. An infra-red detector can receive the data
carried in the infra-red signal and appropriate circuitry can place
the data on bus 602. Bus 602 carries the data to main memory 606,
from which processor 604 retrieves and executes the instructions.
The instructions received by main memory 606 may retrieves and
executes the instructions. The instructions received by main memory
606 may optionally be stored on storage device 610 either before or
after execution by processor 604.
[0070] The computer system 600 also includes a communication
interface 618 coupled to bus 602. Communication interface 618
provides a two-way data communication coupling to one or more
network links that are connected to one or more local networks. For
example, communication interface 618 may be an integrated services
digital network (ISDN) card, cable modem, satellite modem, or a
modem to provide a data communication connection to a corresponding
type of telephone line. As another example, communication interface
618 may be a local area network (LAN) card to provide a data
communication connection to a compatible LAN (or WAN component to
communicated with a WAN). Wireless links may also be implemented.
In any such implementation, communication interface 618 sends and
receives electrical, electromagnetic or optical signals that carry
digital data streams representing various types of information.
[0071] A network link typically provides data communication through
one or more networks to other data devices. For example, a network
link may provide a connection through local network to a host
computer or to data equipment operated by an Internet Service
Provider (ISP). The ISP in turn provides data communication
services through the world wide packet data communication network
now commonly referred to as the "Internet". Local network and
Internet both use electrical, electromagnetic or optical signals
that carry digital data streams. The signals through the various
networks and the signals on network link and through communication
interface 618, which carry the digital data to and from computer
system 600, are example forms of transmission media.
[0072] The computer system 600 can send messages and receive data,
including program code, through the network(s), network link and
communication interface 618. In the Internet example, a server
might transmit a requested code for an application program through
the Internet, the ISP, the local network and the communication
interface 618.
[0073] The received code may be executed by processor 604 as it is
received, and/or stored in storage device 610, or other
non-volatile storage for later execution.
[0074] Each of the processes, methods, and algorithms described in
the preceding sections may be embodied in, and fully or partially
automated by, code modules executed by one or more computer systems
or computer processors comprising computer hardware. The processes
and algorithms may be implemented partially or wholly in
application-specific circuitry.
[0075] The various features and processes described above may be
used independently of one another, or may be combined in various
ways. All possible combinations and sub-combinations are intended
to fall within the scope of this disclosure. In addition, certain
method or process blocks may be omitted in some implementations.
The methods and processes described herein are also not limited to
any particular sequence, and the blocks or states relating thereto
can be performed in other sequences that are appropriate. For
example, described blocks or states may be performed in an order
other than that specifically disclosed, or multiple blocks or
states may be combined in a single block or state. The example
blocks or states may be performed in serial, in parallel, or in
some other manner. Blocks or states may be added to or removed from
the disclosed example embodiments. The example systems and
components described herein may be configured differently than
described. For example, elements may be added to, removed from, or
rearranged compared to the disclosed example embodiments.
[0076] Conditional language, such as, among others, "can," "could,"
"might," or "may," unless specifically stated otherwise, or
otherwise understood within the context as used, is generally
intended to convey that certain embodiments include, while other
embodiments do not include, certain features, elements and/or
steps. Thus, such conditional language is not generally intended to
imply that features, elements and/or steps are in any way required
for one or more embodiments or that one or more embodiments
necessarily include logic for deciding, with or without user input
or prompting, whether these features, elements and/or steps are
included or are to be performed in any particular embodiment.
[0077] Any process descriptions, elements, or blocks in the flow
diagrams described herein and/or depicted in the attached figures
should be understood as potentially representing modules, segments,
or portions of code which include one or more executable
instructions for implementing specific logical functions or steps
in the process. Alternate implementations are included within the
scope of the embodiments described herein in which elements or
functions may be deleted, executed out of order from that shown or
discussed, including substantially concurrently or in reverse
order, depending on the functionality involved, as would be
understood by those skilled in the art.
[0078] It should be emphasized that many variations and
modifications may be made to the above-described embodiments, the
elements of which are to be understood as being among other
acceptable examples. All such modifications and variations are
intended to be included herein within the scope of this disclosure.
The foregoing description details certain embodiments of the
invention. It will be appreciated, however, that no matter how
detailed the foregoing appears in text, the invention can be
practiced in many ways. As is also stated above, it should be noted
that the use of particular terminology when describing certain
features or aspects of the invention should not be taken to imply
that the terminology is being re-defined herein to be restricted to
including any specific characteristics of the features or aspects
of the invention with which that terminology is associated. The
scope of the invention should therefore be construed in accordance
with the appended claims and any equivalents thereof.
Engines, Components, and Logic
[0079] Certain embodiments are described herein as including logic
or a number of components, engines, or mechanisms. Engines may
constitute either software engines (e.g., code embodied on a
machine-readable medium) or hardware engines. A "hardware engine"
is a tangible unit capable of performing certain operations and may
be configured or arranged in a certain physical manner. In various
example embodiments, one or more computer systems (e.g., a
standalone computer system, a client computer system, or a server
computer system) or one or more hardware engines of a computer
system (e.g., a processor or a group of processors) may be
configured by software (e.g., an application or application
portion) as a hardware engine that operates to perform certain
operations as described herein.
[0080] In some embodiments, a hardware engine may be implemented
mechanically, electronically, or any suitable combination thereof.
For example, a hardware engine may include dedicated circuitry or
logic that is permanently configured to perform certain operations.
For example, a hardware engine may be a special-purpose processor,
such as a Field-Programmable Gate Array (FPGA) or an Application
Specific Integrated Circuit (ASIC). A hardware engine may also
include programmable logic or circuitry that is temporarily
configured by software to perform certain operations. For example,
a hardware engine may include software executed by a
general-purpose processor or other programmable processor. Once
configured by such software, hardware engines become specific
machines (or specific components of a machine) uniquely tailored to
perform the configured functions and are no longer general-purpose
processors. It will be appreciated that the decision to implement a
hardware engine mechanically, in dedicated and permanently
configured circuitry, or in temporarily configured circuitry (e.g.,
configured by software) may be driven by cost and time
considerations.
[0081] Accordingly, the phrase "hardware engine" should be
understood to encompass a tangible entity, be that an entity that
is physically constructed, permanently configured (e.g.,
hardwired), or temporarily configured (e.g., programmed) to operate
in a certain manner or to perform certain operations described
herein. As used herein, "hardware-implemented engine" refers to a
hardware engine. Considering embodiments in which hardware engines
are temporarily configured (e.g., programmed), each of the hardware
engines need not be configured or instantiated at any one instance
in time. For example, where a hardware engine comprises a
general-purpose processor configured by software to become a
special-purpose processor, the general-purpose processor may be
configured as respectively different special-purpose processors
(e.g., comprising different hardware engines) at different times.
Software accordingly configures a particular processor or
processors, for example, to constitute a particular hardware engine
at one instance of time and to constitute a different hardware
engine at a different instance of time.
[0082] Hardware engines can provide information to, and receive
information from, other hardware engines. Accordingly, the
described hardware engines may be regarded as being communicatively
coupled. Where multiple hardware engines exist contemporaneously,
communications may be achieved through signal transmission (e.g.,
over appropriate circuits and buses) between or among two or more
of the hardware engines. In embodiments in which multiple hardware
engines are configured or instantiated at different times,
communications between such hardware engines may be achieved, for
example, through the storage and retrieval of information in memory
structures to which the multiple hardware engines have access. For
example, one hardware engine may perform an operation and store the
output of that operation in a memory device to which it is
communicatively coupled. A further hardware engine may then, at a
later time, access the memory device to retrieve and process the
stored output. Hardware engines may also initiate communications
with input or output devices, and can operate on a resource (e.g.,
a collection of information).
[0083] The various operations of example methods described herein
may be performed, at least partially, by one or more processors
that are temporarily configured (e.g., by software) or permanently
configured to perform the relevant operations. Whether temporarily
or permanently configured, such processors may constitute
processor-implemented engines that operate to perform one or more
operations or functions described herein. As used herein,
"processor-implemented engine" refers to a hardware engine
implemented using one or more processors.
[0084] Similarly, the methods described herein may be at least
partially processor-implemented, with a particular processor or
processors being an example of hardware. For example, at least some
of the operations of a method may be performed by one or more
processors or processor-implemented engines. Moreover, the one or
more processors may also operate to support performance of the
relevant operations in a "cloud computing" environment or as a
"software as a service" (SaaS). For example, at least some of the
operations may be performed by a group of computers (as examples of
machines including processors), with these operations being
accessible via a network (e.g., the Internet) and via one or more
appropriate interfaces (e.g., an Application Program Interface
(API)).
[0085] The performance of certain of the operations may be
distributed among the processors, not only residing within a single
machine, but deployed across a number of machines. In some example
embodiments, the processors or processor-implemented engines may be
located in a single geographic location (e.g., within a home
environment, an office environment, or a server farm). In other
example embodiments, the processors or processor-implemented
engines may be distributed across a number of geographic
locations.
Language
[0086] Throughout this specification, plural instances may
implement components, operations, or structures described as a
single instance. Although individual operations of one or more
methods are illustrated and described as separate operations, one
or more of the individual operations may be performed concurrently,
and nothing requires that the operations be performed in the order
illustrated. Structures and functionality presented as separate
components in example configurations may be implemented as a
combined structure or component. Similarly, structures and
functionality presented as a single component may be implemented as
separate components. These and other variations, modifications,
additions, and improvements fall within the scope of the subject
matter herein.
[0087] Although an overview of the subject matter has been
described with reference to specific example embodiments, various
modifications and changes may be made to these embodiments without
departing from the broader scope of embodiments of the present
disclosure. Such embodiments of the subject matter may be referred
to herein, individually or collectively, by the term "invention"
merely for convenience and without intending to voluntarily limit
the scope of this application to any single disclosure or concept
if more than one is, in fact, disclosed.
[0088] The embodiments illustrated herein are described in
sufficient detail to enable those skilled in the art to practice
the teachings disclosed. Other embodiments may be used and derived
therefrom, such that structural and logical substitutions and
changes may be made without departing from the scope of this
disclosure. The Detailed Description, therefore, is not to be taken
in a limiting sense, and the scope of various embodiments is
defined only by the appended claims, along with the full range of
equivalents to which such claims are entitled.
[0089] It will be appreciated that an "engine," "system," "data
store," and/or "database" may comprise software, hardware,
firmware, and/or circuitry. In one example, one or more software
programs comprising instructions capable of being executable by a
processor may perform one or more of the functions of the engines,
data stores, databases, or systems described herein. In another
example, circuitry may perform the same or similar functions.
Alternative embodiments may comprise more, less, or functionally
equivalent engines, systems, data stores, or databases, and still
be within the scope of present embodiments. For example, the
functionality of the various systems, engines, data stores, and/or
databases may be combined or divided differently.
[0090] "Open source" software is defined herein to be source code
that allows distribution as source code as well as compiled form,
with a well-publicized and indexed means of obtaining the source,
optionally with a license that allows modifications and derived
works.
[0091] The data stores described herein may be any suitable
structure (e.g., an active database, a relational database, a
self-referential database, a table, a matrix, an array, a flat
file, a documented-oriented storage system, a non-relational No-SQL
system, and the like), and may be cloud-based or otherwise.
[0092] As used herein, the term "or" may be construed in either an
inclusive or exclusive sense. Moreover, plural instances may be
provided for resources, operations, or structures described herein
as a single instance. Additionally, boundaries between various
resources, operations, engines, engines, and data stores are
somewhat arbitrary, and particular operations are illustrated in a
context of specific illustrative configurations. Other allocations
of functionality are envisioned and may fall within a scope of
various embodiments of the present disclosure. In general,
structures and functionality presented as separate resources in the
example configurations may be implemented as a combined structure
or resource. Similarly, structures and functionality presented as a
single resource may be implemented as separate resources. These and
other variations, modifications, additions, and improvements fall
within a scope of embodiments of the present disclosure as
represented by the appended claims. The specification and drawings
are, accordingly, to be regarded in an illustrative rather than a
restrictive sense.
[0093] Conditional language, such as, among others, "can," "could,"
"might," or "may," unless specifically stated otherwise, or
otherwise understood within the context as used, is generally
intended to convey that certain embodiments include, while other
embodiments do not include, certain features, elements and/or
steps. Thus, such conditional language is not generally intended to
imply that features, elements and/or steps are in any way required
for one or more embodiments or that one or more embodiments
necessarily include logic for deciding, with or without user input
or prompting, whether these features, elements and/or steps are
included or are to be performed in any particular embodiment.
[0094] Although the invention has been described in detail for the
purpose of illustration based on what is currently considered to be
the most practical and preferred implementations, it is to be
understood that such detail is solely for that purpose and that the
invention is not limited to the disclosed implementations, but, on
the contrary, is intended to cover modifications and equivalent
arrangements that are within the spirit and scope of the appended
claims. For example, it is to be understood that the present
invention contemplates that, to the extent possible, one or more
features of any embodiment can be combined with one or more
features of any other embodiment.
* * * * *