U.S. patent application number 14/025616 was filed with the patent office on 2014-03-20 for recommendation machine.
This patent application is currently assigned to iMATCHATIVE Corp.. The applicant listed for this patent is iMATCHATIVE Corp.. Invention is credited to Samuel Mcmillian Hocking, JR., Edward Casteel Milner, Thomas Johannes Oberlechner, Peter R. Paradis, Julia Pitters.
Application Number | 20140081768 14/025616 |
Document ID | / |
Family ID | 50275438 |
Filed Date | 2014-03-20 |
United States Patent
Application |
20140081768 |
Kind Code |
A1 |
Hocking, JR.; Samuel Mcmillian ;
et al. |
March 20, 2014 |
RECOMMENDATION MACHINE
Abstract
A machine may be configured as a recommendation machine that
makes one or more recommendations to one or more users. The machine
may provide a recommendation based on one or more psychometric
profiles that correspond to a user, a product, a manager for the
product, or any suitable combination thereof. The user may be a
potential buyer of the product, and the manager for the product may
fully or partially manage the product and consequently affect the
value of the product. The user's psychometric profile may describe
market understanding and risk tolerance possessed by the user, and
the manager's psychometric profile may describe market
understanding and risk tolerance held by the manager. Such
psychometric profiles may be used to make recommendations of
products. In addition, a psychometric profile for the product
itself may be used in the making of such recommendations.
Inventors: |
Hocking, JR.; Samuel Mcmillian;
(San Francisco, CA) ; Oberlechner; Thomas Johannes;
(San Francisco, CA) ; Milner; Edward Casteel;
(Dallas, TX) ; Paradis; Peter R.; (San Jose,
CA) ; Pitters; Julia; (Vienna, AU) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
iMATCHATIVE Corp. |
San Francisco |
CA |
US |
|
|
Assignee: |
iMATCHATIVE Corp.
San Francisco
CA
|
Family ID: |
50275438 |
Appl. No.: |
14/025616 |
Filed: |
September 12, 2013 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
|
61743930 |
Sep 15, 2012 |
|
|
|
61829146 |
May 30, 2013 |
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Current U.S.
Class: |
705/14.66 |
Current CPC
Class: |
G06Q 30/06 20130101;
G06Q 40/08 20130101; G06Q 40/06 20130101; G06Q 30/0269 20130101;
G06Q 30/0282 20130101; G06Q 30/0631 20130101 |
Class at
Publication: |
705/14.66 |
International
Class: |
G06Q 30/02 20060101
G06Q030/02 |
Claims
1. A method comprising: accessing a first psychometric profile of a
user, the first psychometric profile including a market model of
the user and a risk model of the user, the market model of the user
indicating an unconscious characterization applied by the user to a
market for products, the risk model of the user indicating an
unconscious degree of risk tolerable by the user; accessing a
plurality of psychometric profiles of products within the market, a
second psychometric profile among the plurality of psychometric
profiles being correlated with a product within the market to which
the unconscious characterization is applied; using a processor,
identifying the product based on a comparison of the first
psychometric profile of the user to the second psychometric profile
correlated to the product; and causing a user interface to present
the user with a reference to the product in response to the
identifying of the product based on the comparison of the first and
second psychometric profiles.
2. The method of claim 1 further comprising: generating the first
psychometric profile of the user, the generating of the first
psychometric profile including generating the market model of the
user based on a metaphor statement presented to the user and an
indicated level of agreement by the user with the metaphor
statement, the metaphor statement being a figurative description of
the market.
3. The method of claim 2 further comprising: causing the user
interface to present the metaphor statement on a device of the
user; and receiving the indicated level of agreement by the user
with the presented metaphor statement that figuratively describes
the market.
4. The method of claim 1 further comprising: generating the first
psychometric profile of the user, the generating of the first
psychometric profile including generating the risk model of the
user based on a result of an implicit association test completed by
the user, the result indicating the unconscious degree of risk
tolerable by the user.
5. The method of claim 4 further comprising: administering the
implicit association test by causing the user interface to present
the implicit association test on a device of the user and receive
responses of the user to the implicit association test; and
calculating the result from the received responses of the user to
the administered implicit association test.
6. The method of claim 4, wherein: the result of the implicit
association test is an unprimed result of an unprimed implicit
association test administered to the user; the generating of the
risk model is based on a primed result of a primed implicit
association test administered to the user after the unprimed
implicit association test; and the method further comprises
administering the unprimed implicit association test on a device of
the user and subsequently administering the primed implicit
association test on the device of the user, the unprimed and primed
implicit association tests being administered by the causing the
user interface to present the unprimed and primed implicit
association tests on the device of the user.
7. The method of claim 6 further comprising: causing the user
interface to present priming content to the user between the
unprimed and primed implicit association tests; and wherein the
calculating of the result is based on unprimed responses of the
user to the unprimed implicit association test and based on primed
responses of the user to the primed implicit association test.
8. The method of claim 7, wherein: the result indicates the
unconscious degree of risk tolerable by the user in regard to
financial risk; and the priming content includes a description of a
financial disaster scenario.
9. The method of claim 1 further comprising: generating the second
psychometric profile correlated with the product, the generating of
the second psychometric profile including generating a further
market model of a manager that manages the product, the further
market model indicating an unconscious characterization applied by
the manager to the market, the further market model being generated
based on a metaphor statement presented to the manager and an
indicated level of agreement by the manager with the metaphor
statement, the metaphor statement being a figurative description of
the market.
10. The method of claim 1, wherein: generating the second
psychometric profile correlated with the product, the generating of
the second psychometric profile including generating a further risk
model of a manager that manages the product, the further risk model
indicating a further unconscious degree of risk tolerable by the
manager, the further risk model being generated based on a result
of an implicit association test completed by the manager, the
result indicating the further unconscious degree of risk tolerable
by the manager.
11. The method of claim 10 further comprising: administering the
implicit association test by causing the user interface to present
the implicit association test on a device of the manager and
receive responses of the manager to the implicit association test;
and calculating the result from the received responses of the
manager to the administered implicit association test.
12. The method of claim 11, wherein: the result of the implicit
association test is an unprimed result of an unprimed implicit
association test administered to the manager; the generating of the
risk model is based on a primed result of a primed implicit
association test administered to the manager after the unprimed
implicit association test; and the method further comprises
administering the unprimed implicit association test on a device of
the manager and subsequently administering the primed implicit
association test on the device of the manager, the unprimed and
primed implicit association tests being administered by the causing
the user interface to present the unprimed and primed implicit
association tests on the device of the manager.
13. The method of claim 1 further comprising: causing the user
interface to present a parameter selection bar with a minimum
slider and a maximum slider, the minimum slider being operable to
specify a minimum value of a range for a parameter of the product,
the maximum slider being operable to specify a maximum value of the
range for the parameter of the product, the parameter selection bar
being presented with a corresponding importance bar with an
importance slider operable to specify an importance of the range;
and detecting specification of range and the importance of the
range; and wherein the identifying of the product includes
determining that the parameter of the product falls outside of the
range by a margin but that the product is nonetheless eligible for
identification based on the margin and the importance of the
range.
14. The method of claim 1 further comprising: generating the second
psychometric profile correlated with the product based on
performance statistics indicative of changes in value of the
product.
15. The method of claim 1, wherein: the user is at least one of a
first person, a first team, or a first organization; and the second
psychometric profile describes a manager that is at least one of a
second person that manages the product, a second team that manages
the product, or a second organization that manages the product.
16. The method of claim 1, wherein: the product is an investment
fund; the user is a potential investor in the investment fund; and
the second psychometric profile describes a manager that manages
the investment fund.
17. A non-transitory machine-readable storage medium comprising
instructions that, when executed by one or more processors of a
machine, cause the machine to perform operations comprising:
accessing a first psychometric profile of a user, the first
psychometric profile including a market model of the user and a
risk model of the user, the market model of the user indicating an
unconscious characterization applied by the user to a market for
products, the risk model of the user indicating an unconscious
degree of risk tolerable by the user; accessing a plurality of
psychometric profiles of products within the market, a second
psychometric profile among the plurality of psychometric profiles
being correlated with a product within the market to which the
unconscious characterization is applied; identifying the product
based on a comparison of the first psychometric profile of the user
to the second psychometric profile correlated to the product; and
causing a user interface to present the user with a reference to
the product in response to the identifying of the product based on
the comparison of the first and second psychometric profiles.
18. The non-transitory machine-readable storage medium of claim 17,
wherein the operations further comprise: generating the first
psychometric profile of the user, the generating of the first
psychometric profile including generating the market model of the
user based on a metaphor statement presented to the user and an
indicated level of agreement by the user with the metaphor
statement, the metaphor statement being a figurative description of
the market.
19. A system comprising: an access module configured to: access a
first psychometric profile of a user, the first psychometric
profile including a market model of the user and a risk model of
the user, the market model of the user indicating an unconscious
characterization applied by the user to a market for products, the
risk model of the user indicating an unconscious degree of risk
tolerable by the user; access a plurality of psychometric profiles
of products within the market, a second psychometric profile among
the plurality of psychometric profiles being correlated with a
product within the market to which the unconscious characterization
is applied; a processor configured by a recommender module to
identify the product based on a comparison of the first
psychometric profile of the user to the second psychometric profile
correlated to the product; and a user interface module configured
to cause a user interface to present the user with a reference to
the product in response to the identifying of the product based on
the comparison of the first and second psychometric profiles.
20. The system of claim 19 further comprising: a user analysis
module configured to generate the first psychometric profile of the
user, the generating of the first psychometric profile including
generating the risk model of the user based on a result of an
implicit association test completed by the user, the result
indicating the unconscious degree of risk tolerable by the user.
Description
RELATED APPLICATIONS
[0001] This application claims the priority benefits of U.S.
Provisional Patent Application No. 61/743,930, filed Sep. 15, 2012,
and U.S. Provisional Patent Application No. 61/829,146, filed May
30, 2013, both of which are incorporated herein by reference in
their entirety.
TECHNICAL FIELD
[0002] The subject matter disclosed herein generally relates to the
processing of data. Specifically, the present disclosure addresses
systems and methods to facilitate making recommendations.
BACKGROUND
[0003] A product may be available for purchase from a seller. A
product may take the form of a good (e.g., a physical object), a
service (e.g., performed by a service provider), information (e.g.,
downloadable digital media), a license (e.g., authorization to
access something or do something), or any suitable combination
thereof. An item may be a specimen (e.g., an individual instance)
of the product, and multiple items may constitute multiple
specimens of the product. A machine may be configured (e.g., by
special software) to provide a user with a recommendation of a
product of which one or more specimens may be available for to
purchase. Such a machine may form all or part of a network-based
system. Examples of network-based systems include commerce systems
(e.g., systems that host shopping websites or auction websites),
publication systems (e.g., systems that host classified
advertisement websites), listing systems (e.g., systems that host
wish list websites or gift registries), transaction systems (e.g.,
systems that host payment websites), and social network systems
(e.g., Facebook.RTM., Twitter.RTM., or LinkedIn.RTM.).
BRIEF DESCRIPTION OF THE DRAWINGS
[0004] Some embodiments are illustrated by way of example and not
limitation in the figures of the accompanying drawings.
[0005] FIG. 1 is a network diagram illustrating a network
environment suitable for a recommendation machine, according to
some example embodiments.
[0006] FIG. 2 is a block diagram illustrating components of a
recommendation machine suitable for making recommendations,
according to some example embodiments.
[0007] FIG. 3 is a conceptual diagram illustrating relationships
among a user, a product, and a manager of the product, as well as
their associated psychometric profiles, according to some example
embodiments.
[0008] FIG. 4 is a conceptual diagram illustrating the
relationships depicted in FIG. 3, as applied to groups of multiple
users and groups of multiple managers, according some example
embodiments.
[0009] FIG. 5 is a conceptual diagram illustrating generation of
psychometric profiles for one or more persons, such as a user or a
manager, according to some example embodiments.
[0010] FIG. 6 is a conceptual diagram illustrating generation of a
psychometric profile for a product, according to some example
embodiments.
[0011] FIG. 7 is a block diagram illustrating a psychometric
profile, according to some example embodiments.
[0012] FIG. 8-15 are flowcharts illustrating operations of the
recommendation machine in performing a method of making a
recommendation, according to some example embodiments.
[0013] FIG. 16 is a screenshot illustrating a user interface with
parameter selection sliders, according to some example
embodiments.
[0014] FIG. 17 is a block diagram illustrating components of a
machine, according to some example embodiments, able to read
instructions from a machine-readable medium and perform any one or
more of the methodologies discussed herein.
DETAILED DESCRIPTION
[0015] Example methods and systems are directed to identification
of one or more media sources. Examples merely typify possible
variations. Unless explicitly stated otherwise, components and
functions are optional and may be combined or subdivided, and
operations may vary in sequence or be combined or subdivided. In
the following description, for purposes of explanation, numerous
specific details are set forth to provide a thorough understanding
of example embodiments. It will be evident to one skilled in the
art, however, that the present subject matter may be practiced
without these specific details.
[0016] A machine may be configured (e.g., by special software) as a
recommendation machine that makes one or more recommendations. The
machine may form all or part of a network-based recommendation
system configured to provide one or more recommendation services
that make recommendations to one or more users. For clarity,
example embodiments of the machine are described herein in the
context of making recommendations of financial products, such as
hedge funds, though other example embodiments of the machine may be
configured to perform similar operations for making recommendations
of other products. The machine may provide a recommendation based
on one or more psychometric profiles that correspond to a user, a
product, a manager for the product, or any suitable combination
thereof. Where the product is a financial product (e.g., an
investment fund, such as a hedge fund), the user may be a person or
group of persons (e.g., a team or a company) acting as a potential
buyer of the product (e.g., as a prospective investor in the hedge
fund). The manager for the product may be a person or group of
persons that fully or partially manages the product and whose
decisions may affect the value of the product (e.g., increase or
decrease the value of the hedge fund).
[0017] A psychometric profile includes (e.g., contains) market
model, risk model, or both. The market model describes the market
for the product, including an implicit or unconscious psychological
understanding of the market. The risk model indicates an implicit
or unconscious psychological preference for risk tolerance (e.g.,
risk-taking or safety-seeking) Hence, the user's psychometric
profile may describe market understanding and risk tolerance
possessed by the user, and the manager's psychometric profile may
describe market understanding and risk tolerance held by the
manager. Such psychometric profiles may be generated, stored, and
accessed by the recommendation machine, and used to make
recommendations of products (e.g., financial products, such as
hedge funds). In addition, a psychometric profile (e.g., a
quasi-psychometric profile) for the product itself may be
generated, stored, and accessed by the recommendation machine, and
used in the making of such recommendations.
[0018] FIG. 1 is a network diagram illustrating a network
environment 100 suitable for a recommendation machine 110,
according to some example embodiments. The network environment 100
includes the recommendation machine 110, a database 115, and
devices 130 and 150, all communicatively coupled to each other via
a network 190. The recommendation machine 110, the database 115,
and the devices 130 and 150 may each be implemented in a computer
system, in whole or in part, as described below with respect to
FIG. 17. As shown, the recommendation machine 110, with or without
the database 115, may form all or part of a network-based system
105 (e.g., a cloud-based product recommendation system, such as a
cloud-based financial product recommendation system).
[0019] Also shown in FIG. 1 are users 132 and 152. One or both of
the users 132 and 152 may be a human user (e.g., a human being) or
a combination of a human user and a machine (e.g., a human assisted
by a machine or a machine supervised by a human, locally or
remotely). The user 132 is not part of the network environment 100,
but is associated with the device 130 and may be a user of the
device 130. For example, the device 130 may be a desktop computer,
a vehicle computer, a tablet computer, a navigational device, a
portable media device, or a smart phone belonging to the user 132.
Likewise, the user 152 is not part of the network environment 100,
but is associated with the device 150. As an example, the device
150 may be a desktop computer, a vehicle computer, a tablet
computer, a navigational device, a portable media device, or a
smart phone belonging to the user 152.
[0020] Any of the machines, databases, or devices shown in FIG. 1
may be implemented in a general-purpose computer modified (e.g.,
configured or programmed) by special software to be a
special-purpose computer to perform one or more of the functions
described herein for that machine, database, or device. For
example, a computer system able to implement any one or more of the
methodologies described herein, in full or in part, is discussed
below with respect to FIG. 17. As used herein, a "database" is a
data storage resource and may store data structured as a text file,
a table, a spreadsheet, a relational database (e.g., an
object-relational database), a triple store, a hierarchical data
store, or any suitable combination thereof. Moreover, any two or
more of the machines, databases, or devices illustrated in FIG. 1
may be combined into a single machine, and the functions described
herein for any single machine, database, or device may be
subdivided among multiple machines, databases, or devices.
[0021] The network 190 may be any network that enables
communication between or among machines, databases, and devices
(e.g., the server machine 110 and the device 130). Accordingly, the
network 190 may be a wired network, a wireless network (e.g., a
mobile or cellular network), or any suitable combination thereof.
The network 190 may include one or more portions that constitute a
private network, a public network (e.g., the Internet), or any
suitable combination thereof. Accordingly, the network 190 may
include one or more portions that incorporate a local area network
(LAN), a wide area network (WAN), the Internet, a mobile telephone
network (e.g., a cellular network), a wired telephone network
(e.g., a plain old telephone system (POTS) network), a wireless
data network (e.g., WiFi network or WiMax network), or any suitable
combination thereof. Any one or more portions of the network 190
may communicate information via a transmission medium. As used
herein, "transmission medium" shall be taken to include any
intangible medium that is capable of storing, encoding, or carrying
instructions for execution by a machine, and includes digital or
analog communication signals or other intangible media to
facilitate communication of such software.
[0022] FIG. 2 is a block diagram illustrating components of the
recommendation machine 110, according to some example embodiments.
The recommendation machine 110 is shown as including an access
module 210, a user interface module 220, the user analysis module
230, a product analysis module 240, and a recommender module 280,
all configured to communicate with each other (e.g., via a bus,
shared memory, or a switch).
[0023] The access module 210 may be configured to access
information from various sources, such as the database 115, the
device 130, the device 150, or any suitable combination thereof.
The user interface module 220 may be configured to cause a user
interface (e.g., the user interface of an application or app
executing on the client device 130) to present a reference to a
product (e.g., a search result, a report, or an advertisement that
references the product. Such a reference may be a recommendation of
the product, may function as a recommendation of the product, and
may be presented as a recommendation of the product.
[0024] As shown in FIG. 2, the user analysis module 230 may include
a market metaphor module 232, risk module 234, and a group module
236, all configured to communicate with each other. The risk module
234 is shown as including an implicit association module 235. In
the context of recommending financial products (e.g., hedge funds),
the users 132 and 152 may be treated as potential or actual
investors, and the user analysis module 230 may be configured as an
investor analysis module.
[0025] Similarly, the product analysis module 240 may include a
market metaphor module 242, risk module 244, and a group module
246, all configured to communicate with each other. The risk module
244 is shown as including an implicit association module 245. In
the context of recommending financial products (e.g., investments),
the product analysis module 240 may be configured as an investment
analysis module.
[0026] The recommender module 280 is shown in FIG. 2 as including a
search module 250, product match module 260, and alternative
product module 270, all configured to communicate with each other.
In the context of recommending financial products (e.g.,
investments), the search module 250 may be configured as an
investment search module (e.g., for implementing an investment
search engine); the product match module 260 may be configured as
an investment match module (e.g., for implementing an investment
matching service); and the alternative product module 270 may be
configured as an alternative investment module (e.g., a recommender
of alternative investments, given an initial investment as
input).
[0027] Any one or more of the modules described herein may be
implemented using hardware (e.g., a processor of a machine) or a
combination of hardware and software. For example, any module
described herein may configure a processor to perform the
operations described herein for that module. Moreover, any two or
more of these modules may be combined into a single module, and the
functions described herein for a single module may be subdivided
among multiple modules. Furthermore, according to various example
embodiments, modules described herein as being implemented within a
single machine, database, or device may be distributed across
multiple machines, databases, or devices.
[0028] FIG. 3 is a conceptual diagram illustrating relationships
among a user 310 (e.g., user 132 or user 152), a product 320 (e.g.,
an investment, such as a hedge fund), and a manager 330 of the
product 320, as well as their associated psychometric profiles 312,
322, and 332, according to some example embodiments. As shown in
FIG. 3, the user 310 may be an investor, shopper, or other
potential buyer for the product 320. The manager 330 may be a
person whose decisions affect the value (e.g., a market value that
represents worth, such as a price) of the product 320 (e.g., a
financial manager that decides when to buy and sell assets and
securities within the hedge fund).
[0029] The psychometric profile 312 of the user 310 describes
market understanding (e.g., unconscious) held by the user 310,
indicates psychological preferences (e.g., unconscious) of the user
310 for risk tolerance (e.g., risk-taking or safety-seeking), or
both. The psychometric profile 312 may be self-reported by the user
310, inferred by the recommendation machine 110 (e.g., by the user
analysis module 230) based on detected behavior of the user 310, or
any suitable combination thereof
[0030] Likewise, the psychometric profile 332 of the manager 330
describes market understanding (e.g., unconscious) held by the
manager 330, indicates psychological preferences (e.g.,
unconscious) of the manager 330 for risk tolerance (e.g.,
risk-taking or safety-seeking), or both. The psychometric profile
332 may be self-reported by the manager 330, inferred by the
recommendation machine 110 (e.g., by the product analysis module
240) based on detected behavior of the manager 330, or any suitable
combination thereof
[0031] Although the product 320 is not a person, the psychometric
profile 322 of the product 320 may be a quasi-psychometric profile
of the product 320 itself, where the psychometric profile 322 is
usable as a functional equivalent of a psychometric profile (e.g.,
psychometric profile 332) for a person. The psychometric profile
322 of the product 320 may describe an inferred market
understanding, inferred preferences for risk tolerance, or both.
The psychometric profile 322 may be inferred (e.g., approximated,
estimated, or otherwise derived) by the recommendation machine 110
(e.g., by the product analysis module 240) based on statistical
performance of the product 320 (e.g., statistical data describing
gains, losses, sales of assets, acquisitions of assets, or any
suitable combination thereof, with respect to the product 320).
[0032] FIG. 4 is a conceptual diagram illustrating the
relationships depicted in FIG. 3, as applied to groups of multiple
users and groups of multiple managers, according some example
embodiments. As shown in the top portion of FIG. 4, a group 410 of
users (e.g., an investor team) may function like a single user
(e.g., user 310) with respect to researching products and shopping
for products. A single psychometric profile 412 may be associated
with (e.g., assigned to) the entire group 410 as a whole.
Accordingly, the psychometric profile 412 may describe market
understanding (e.g., unconscious) held by the group 410, indicate
psychological preferences (e.g., unconscious) of the group 410 for
risk tolerance (e.g., risk-taking or safety-seeking), or any
suitable combination thereof. Moreover, the psychometric profile
412 may be self-reported by the group 410, inferred by the
recommendation machine 110 (e.g., by the user analysis module 230)
based on detected behavior of the group 410, or any suitable
combination thereof
[0033] As also shown in the top portion of FIG. 4, a group 430 of
managers (e.g., a management team) may function like a single
manager (e.g., manager 330) with respect to managing products and
making decisions that affect their value. A single psychometric
profile 432 may be associated with (e.g., assigned to) the entire
group 430 as a whole. Accordingly, the psychometric profile 432 may
describe market understanding (e.g., unconscious) held by the group
430, indicate psychological preferences (e.g., unconscious) of the
group 430 for risk tolerance (e.g., risk-taking or safety-seeking),
or any suitable combination thereof. Moreover, the psychometric
profile 432 may be self-reported by the group 430, inferred by the
recommendation machine 110 (e.g., by the product analysis module
240) based on detected behavior of the group 430, or any suitable
combination thereof
[0034] Furthermore, the top portion of FIG. 4 shows that a product
420 (e.g., an investment, such as a hedge fund) may have its own
psychometric profile 422, which may be a quasi-psychometric profile
of the product 420 itself. The psychometric profile 422 of the
product 420 may describe an inferred market understanding, inferred
preferences for risk tolerance, or both. The psychometric profile
422 may be inferred by the recommendation machine 110 (e.g., by the
product analysis module 240) based on statistical performance of
the product 420.
[0035] As shown in the bottom portion of FIG. 4, a user entity 460
(e.g., corporation or other business organization) may function
like a single user (e.g., user 310) with respect to researching
products and shopping for products. A single psychometric profile
462 may be associated with (e.g., assigned to) the entire user
entity 460 as a whole. Accordingly, the psychometric profile 462
may describe market understanding (e.g., unconscious) held by the
user entity 460, indicate psychological preferences (e.g.,
unconscious) of the user entity 460 for risk tolerance, or any
suitable combination thereof. Moreover, the psychometric profile
462 may be self-reported by the user entity 460, inferred by the
recommendation machine 110 (e.g., by the user analysis module 230)
based on detected behavior of the user entity 460, or any suitable
combination thereof
[0036] As also shown in the bottom portion of FIG. 4, a managing
entity 480 (e.g., a management company) may function like a single
manager (e.g., manager 330) with respect to managing products and
making decisions that affect their value. A single psychometric
profile 482 may be associated with (e.g., assigned to) the entire
managing entity 480 as a whole. Accordingly, the psychometric
profile 482 may describe market understanding (e.g., unconscious)
held by the managing entity 480, indicate psychological preferences
(e.g., unconscious) of the managing entity 480 for risk tolerance,
or any suitable combination thereof. Moreover, the psychometric
profile 482 may be self-reported by the managing entity 480,
inferred by the recommendation machine 110 (e.g., by the product
analysis module 240) based on detected behavior of the managing
entity 480, or any suitable combination thereof
[0037] Furthermore, the bottom portion of FIG. 4 shows that a
product 470 (e.g., an investment, such as a hedge fund) may have
its own psychometric profile 472, which may be a quasi-psychometric
profile of the product 470 itself. The psychometric profile 472 of
the product 470 may describe an inferred market understanding,
inferred preferences for risk tolerance, or both. The psychometric
profile 472 may be inferred by the recommendation machine 110
(e.g., by the product analysis module 240) based on statistical
performance of the product 470.
[0038] FIG. 5 is a conceptual diagram illustrating generation of a
psychometric profile (e.g., psychometric profile 312, 332, 412,
432, 462, or 482) for one or more persons (e.g., humans), such as
the user 310, the manager 330, the group 410 of users, the group
430 of managers, the user entity 460, or the manager entity 480,
according to some example embodiments. As described in further
detail below, generation of a psychometric profile (e.g.,
psychometric profile 312) may be based on one or more inputs. After
generation, psychometric profiles (e.g., psychometric profile 312)
may be stored in the database 115, for subsequent access therefrom
(e.g., by the access module 210 of the recommendation machine
110).
[0039] As shown in FIG. 5, examples of such inputs include one or
more metaphor statements, such as a metaphor statement 510, with or
without its corresponding influence strength 512, and a further
metaphor statement 520, with or without its corresponding influence
strength 522. Other examples of such inputs include one or more
implicit association test (IAT) results, such as an IAT result 530
(e.g., resulting from an IAT administered without priming) and
another IAT result 540 (e.g., resulting from an IAT administered
with priming).
[0040] A further example of such inputs is preferences 550 of the
person for whom the psychometric profile (e.g., psychometric
profile 312) is being generated. The preferences 550 may indicate,
specify, define, or constrain attributes or characteristics of
products. In the context of financial products (e.g., hedge funds),
the preferences 550 may indicate attributes such as specific fees
(e.g., a management fee, a performance fee, or a redemption fee),
costs, assets (e.g., identifiers of the individual investment
instruments, such as stocks, or categories thereof), an investment
type (e.g., hedge fund, mutual fund, or exchange-traded fund), an
investment size (e.g., expressed as a current value of all assets),
an investment style (e.g., a name of an investment strategy, or a
category thereof), or any suitable combination thereof.
[0041] A yet further example of such inputs is behavior data 560 of
the person for whom the psychometric profile (e.g., psychometric
profile 312) is being generated. The behavior data 560 may
indicate, record, track, or reference historical behavior of the
person. For example, the behavior data 560 may include a search
history of a person, an investment history of the person, or both.
As shown in FIG. 5, one or more of these inputs may be used (e.g.,
by the recommendation machine 110) to generate or update
psychometric profiles (e.g., psychometric profiles 312, 332, 412,
432, 462, and 482), which may be stored in the database 115 for
access by the recommendation machine 110.
[0042] FIG. 6 is a conceptual diagram illustrating generation of a
psychometric profile (e.g., psychometric profile 322, 422, or 472)
for a product (e.g., product 320, 420, or 470), according to some
example embodiments. As described in further detail below,
generation of a psychometric profile (e.g., psychometric profile
322, which may be a quasi-psychometric profile) correlated with a
product (e.g., product 320) may be based on statistical data 610
(e.g., performance statistics) that indicates, records, tracks, or
references historical events indicative of changes in the value of
the product (e.g., product 320). For example, the statistical data
610 may include information about past returns (e.g., absolute
returns, gains, losses, or any suitable combination thereof), fees,
costs, financial ratios (e.g., Sharpe ratio), or any suitable
combination thereof.
[0043] FIG. 7 is a block diagram illustrating the psychometric
profile 312, according to some example embodiments. One or more of
the psychometric profiles 322, 332, 412, 422, 432, 462, 472, and
482 may be structured similarly to the psychometric profile 312.
The psychometric profile 312, as noted above, corresponds to the
user 310 and may indicate one or more measurable psychological
attributes, traits, characteristics, or propensities (e.g.,
including unconscious propensities) of the user 310. As shown, the
psychometric profile 312 includes a market model 710 and a risk
model 720, though the psychometric profile 312 may also contain
additional psychological data in reference to the user 310.
[0044] The market model 710 may be or include a figurative
description of a market for products (e.g., product 320). Such a
description may take the form of a metaphor statement to which the
user 310 has expressed agreement or to which the user 310 is likely
to agree. As such, the description of the market (e.g., a metaphor
statement) may be or include an implicit or unconscious
psychological understanding of the market. In the context of making
recommendations of financial products, examples of metaphor
statements include: "investing is competing against others and
trying to beat them," "the market is rational," "investing is like
placing bets on a gamble," "the market is a bazaar where people
shop to find the best price," "the market has a mind of its own,"
"investing in the market is like navigating on ocean," "the market
can be a dangerous minefield," and "in the market, battles are won
and lost." Accordingly, the market model 710 may indicate an
implicit or unconscious characterization applied by the user 310 to
the market for products.
[0045] The risk model 720 may be or include one or more IAT results
(e.g., IAT results 530 and 540). The results of an IAT indicate how
strongly the test-taker (e.g., the user 310) has associated various
concepts in his or her mind. In the context of making financial
product recommendations, such IAT results may indicate an implicit
or unconscious psychological preference for a certain degree of
risk tolerance (e.g., a level of risk-taking or safety-seeking)
Accordingly, the risk model 720 may indicate an implicit or
unconscious degree of risk that is tolerable by the user 310 (e.g.,
within the market for products).
[0046] FIG. 8-15 are flowcharts illustrating operations of the
recommendation machine 110 in performing a method 800 of making a
recommendation of a product (e.g., product 320), according to some
example embodiments. Operations in the method 800 may be performed
by the recommendation machine 110 (e.g., by a processor therein)
using modules described above with respect to FIG. 2. As shown in
FIG. 8, the method 800 may include one or more of operations 810,
820, and 830, operation 840, one or more of operations 850 and 860,
and operation 870.
[0047] In operation 810, the access module 210 accesses (e.g., from
the database 115) a psychometric profile (e.g., psychometric
profile 312, 412, or 462) of a user (e.g., user 310, group 410, or
user entity 460). For example, the access module 210 may access the
psychometric profile 312 for the user 310. As noted above, the
psychometric profile 312 may include the market model 710 and the
risk model 720. As also noted above, the market model 710 may
indicate an unconscious characterization applied by the user 310 to
a market for products (e.g., products 320, 420, and 470), and the
risk model 720 may indicate an unconscious degree of risk tolerable
by the user 310.
[0048] In operation 820, the access module 210 accesses (e.g., from
the database 115) psychometric profiles (e.g., psychometric profile
322, 422, or 472, which may be quasi-psychometric profiles) of
multiple products (e.g., products 320, 420, and 470) available for
recommendation, purchase, or both, within the market for products.
For example, the access module 210 may access the psychometric
profile 322 of the product 320, as well as additional psychometric
profiles of additional products.
[0049] In operation 830, the access module 210 accesses (e.g., from
the database 115) psychometric profiles (e.g., psychometric
profiles 332, 432, and 482) of various managers (e.g., manager 330,
group 430, or managing entity 480) for various products (e.g.,
products 320, 420, and 470) available for recommendation, purchase,
or both, within the market for products. For example, the access
module 210 may access the psychometric profile 332 of the manager
330, as well as additional psychometric profiles of additional
managers for additional products.
[0050] In operation 840, the recommender module 280 performs
comparisons among the psychometric profiles accessed in one or more
of operations 810, 820, 830. For example, the recommender module
280 may compare the psychometric profile 312 of the user 310 with
the psychometric profile 332 of the manager 330 and other
psychometric profiles of other managers. As another example, the
psychometric profile 312 may be compared with the psychometric
profile 322 of the product 320 and other psychometric profiles of
other products. These comparisons enable identification of one or
more psychometric profiles (e.g., psychometric profile 322 or 332)
that match or are similar to the psychometric profile 312 of the
user 310, as well as identification of the products (e.g., product
320) that are correlated with (e.g., that correspond to) the
matched or similar psychometric profiles.
[0051] In operation 850, the recommender module 280 (e.g., via the
search module 250) identifies the product 320 as a search result,
in response to a previously submitted search (e.g., search request)
submitted by the user whose psychometric profile was accessed in
operation 810 (e.g., user 310, group 410, or user entity 460). For
example, the search may have been previously submitted by the user
310 (e.g., by the user 132 via the device 130). The product 320 may
be identified as a match to search criteria submitted by the user
(e.g., a product whose psychometric profile matches the search
criteria, a product whose manager has a psychometric profile that
matches the search criteria, or both). This identification of the
product 320 may be performed based on one or more of the
comparisons performed in operation 840.
[0052] In operation 860, the recommender module 280 (e.g., via the
product match module 260) identifies the product 320 as a match for
the user whose psychometric profile was accessed in operation 810
(e.g., user 310). The product 320 may be identified as a
user-to-product match (e.g., a product whose psychometric profile
exactly, closely, or sufficiently matches the psychometric profile
of the user), a user-to-manager match (e.g., a product whose
manager, group of managers, or managing entity has a psychometric
profile that exactly, closely, or sufficiently matches a
psychometric profile the user), or both. This identification of the
product 320 may be likewise performed based on (e.g., by
performing) one or more of the comparisons performed in operation
840.
[0053] In operation 870, the user interface module 220 causes a
user interface to present a reference to the product 320 to the
user whose psychometric profile was accessed in operation 810
(e.g., user 310). For example, the user interface may form all or
part of an application (e.g., a browser application or other
interactive software) executing on the device 130, and the user
interface module 220 may cause the user interface to present the
reference to the product 320 on a display of the device 130.
Moreover, the reference may be presented as a recommendation of the
product 320. According to various example embodiments, the
reference may take the form of a search result, an advertisement, a
listing, a hyperlink, a suggestion, or other notification that
references the product 320 (e.g., by name or other identifier, such
as a ticker symbol). Operation 870 may be performed in response to
operation 850, operation 860, or any suitable combination
thereof
[0054] According to some example embodiments, a product (e.g.,
product 320) may be specified as an example for finding similar
products that are suitable as alternatives (e.g., product 420).
Accordingly, the product may have a corresponding (e.g.,
associated) psychometric profile (e.g., of the product itself, or
its manager), which may be used in a manner similar to that
described above for the psychometric profile 312 of the user
310.
[0055] As shown in FIG. 9, the method 800 may include one or more
of operations 920, 960, and 970. Operation 920 is similar to
operation 820 in that the access module 210 accesses a psychometric
profile of a product (e.g., psychometric profile 322 of the product
320). However, in operation 920 the access module 210 accesses the
psychometric profile of a first product (e.g., input product) that
has been identified by a previously received submission from a user
(e.g., from user 152 via the device 150). For example, the access
module 210 may access (e.g., from the database 115) the
psychometric profile 322 of the product 320 in response to a
submission that identifies the product 320 (e.g., a request to
identify other products whose associated psychometric profiles
(e.g., of the products themselves, or of their managers) match the
psychometric profile 322 of the product 320). The accessed
psychometric profile the first product may therefore be used in
operation 840 for comparison with other psychometric profiles.
[0056] Operation 960 is similar to operation 860 in that the
recommender module 280 identifies a product (e.g., product 420) as
a match to something. However, in operation 950, the recommender
module 280 identifies a second product (e.g., output product) as a
match for the first product (e.g., input product) discussed above
with respect operation 920. For example, the product 420 may be
identified as a product-to-product match (e.g., a product whose
psychometric profile exactly, closely, or sufficiently matches the
psychometric profile of the first product), a product-to-manager
match (e.g., a product whose manager, group of managers, or
managing entity has a psychometric profile that exactly, closely,
or sufficiently matches the psychometric profile the first
product), a manager-to-manager match (e.g., a product whose
manager, group of managers, or managing entity has a psychometric
profile that exactly, closely, or sufficiently matches the
psychometric profile of the manager, group of managers, or managing
entity of the first product), or any suitable combination thereof.
This identification of the product 420 may be likewise performed
based on (e.g., by performing) one or more of the comparisons
performed in operation 840.
[0057] Operation 970 is similar to operation 870 in that the user
interface module 220 causes the user interface (e.g., executing on
the client device 130) to present a reference to a product.
However, in operation 970, the user interface module 220 causes the
user interface to present a reference to the second product (e.g.,
product 420, which may be an output product) identified in
operation 960. This reference may be presented to a user (e.g., to
the user 152 via the device 150). The user interface may form all
or part of an application (e.g., a browser application) executing
on the device 150, and the user interface module 220 may cause the
user interface to present the reference to the second product on a
display of the device 150. Moreover, the reference may be presented
as a recommendation of the second product. According to various
example embodiments, the reference may take the form of a search
result, an advertisement, a listing, a hyperlink, a suggestion, or
other notification that references the second product (e.g., by
name or other identifier, such as a ticker symbol). Operation 970
may be performed in response to operation 960.
[0058] According to certain example embodiments, the recommendation
machine 110 is configured to generate or update a psychometric
profile for a person (e.g., psychometric profile 312 for the user
310 or psychometric profile 332 for the manager 330), for a group
of persons (e.g., psychometric profile 412 for the group 410 of
users, or psychometric profile 432 for the group 430 of managers),
or for an entity (e.g., psychometric profile 462 for the user
entity 460, or psychometric profile 482 for the managing entity
480) by generating or updating a market model (e.g., market model
710) for the person, group, or entity being modeled by the
psychometric profile. A person (e.g., user 152) may be considered
as a test subject in the administration of one or more tests (e.g.,
an IAT or a test for agreement with various metaphor statements) to
obtain results that may be incorporated into his or her
psychometric profile.
[0059] As shown in FIG. 10, the method 800 may include one or more
of operations 1010, 1020, 1030, 1040, and 1050. In operation 1010,
the user interface module 220 causes the user interface to present
a metaphor statement (e.g., metaphor statement 510 or "The market
is a war zone.") to the test subject (e.g., to the user 152, via a
display on the device 150) and ask that the test subject indicate
his or her level of agreement with the metaphor statement. The user
interface module 220 may further receive the indicated level of
agreement, as a submission by the test subject (e.g., from the
device 150).
[0060] In operation 1020, based on this indicated level of
agreement, an influence strength of the metaphor statement
presented in operation 1010 may be calculated (e.g., as influence
strength 512 for the metaphor statement 510). Where the test
subject is the user 310, the group 410 of users, or the user entity
460, operation 1020 may be performed by the user analysis module
230 (e.g., via the market metaphor module 232). Where the test
subject is the manager 330, the group 430 of managers, or the
managing entity 480, operation 1020 may be performed by the product
analysis module 240 (e.g., via the market metaphor module 242). In
either case, the influence strength may be a normalized value that
represents a relative degree of influence that the metaphor
statement holds with respect to the test subject, in comparison to
other metaphor statements. For example, the influence strength may
be normalized to values between zero and one, where zero represents
no influence on the test subject, and one represents a maximum
influence on the test subject. As shown in FIG. 10, operations 1010
at 1020 may be repeated so that several different metaphor
statements are presented to the test subject and their respective
influence strengths may be calculated.
[0061] In operation 1030, a psychometric profile is generated or
updated for the test subject (e.g., psychometric profile 312 or
332). As shown in FIG. 10, this may be accomplished by generating
or updating a market model (e.g., market model 710) for the test
subject, based on the influence strength (e.g., influence strength
512) of one or more market metaphors (e.g., metaphor statement
510). This market model may then be included in the resulting
psychometric profile. Where the test subject is the user 310, the
group 410 of users, or the user entity 460, operation 1030 may be
performed by the user analysis module 230 (e.g., via the market
metaphor module 232). Where the test subject is the manager 330,
the group 430 of managers, or the managing entity 480, operation
1030 may be performed by the product analysis module 240 (e.g., via
the market metaphor module 242).
[0062] In situations where the psychometric profile (e.g.,
psychometric profile 412 or 452) models a set of multiple
individual users (e.g., group 410 of users, or user entity 460),
the group module 236 within the user analysis module 230 may
aggregate respective market models for each of the individual users
and generate or update a market model for the entire set.
Similarly, where the psychometric profile (e.g., psychometric
profile 432 or 482) models a set of multiple individual managers
(e.g., group 430 of managers, or managing entity 480), the group
module 246 within the product analysis module 240 may aggregate
respective market models for each of the individual managers and
generate or update a market model for the set as a whole.
[0063] In some example embodiments, the test subject may be
prompted to accept, edit, or confirm the resulting psychometric
profile. Accordingly, in operation 1040, the user interface module
220 may present this psychometric profile (e.g., psychometric
profile 312) to the test subject (e.g., user 152, via the device
150). The user interface module 220 may further receive a response
that indicates such acceptance, editing, or confirmation by the
test subject (e.g., from the device 150).
[0064] In operation 1050, the user analysis module 230 stores the
psychometric profile of the test subject (e.g., psychometric
profile 312) in the database 115. Accordingly, the database 115 may
store the psychometric profile for subsequent access by the access
module 210 in performing operation 810, operation 830, or both. As
shown in FIG. 10, one or more of operations 810 and 830 may then be
performed as previously described above with respect to FIG. 8.
[0065] According to various example embodiments, a psychometric
profile for a person, a group of persons, or an entity (e.g.,
psychometric profile 312 for the user 310, or psychometric profile
332 for the manager 330) may be generated or updated by generating
or updating a risk model (e.g., risk model 720). This may be
accomplished by presenting (e.g., administering) one or more IAT's
to the test subject (e.g., a first unprimed IAT, followed by a
presentation of priming content, followed by a second primed
IAT).
[0066] As shown in FIG. 11, the method 800 may include one or more
of operations 1110, 1118, and 1120. In operation 1110, the user
interface module 220 causes the user interface to present (e.g.,
administer) an IAT (e.g., a first, unprimed IAT) to the test
subject (e.g., to the user 152, via a display on the device 150).
The user interface module 220 may further receive responses of the
test subject to the presented IAT (e.g., from the device 150).
Where the test subject is the user 310, the group 410 of users, or
the user entity 460, the user analysis module 230 (e.g., via the
implicit association module 235) may calculate one or more results
(e.g., unprimed results) of the IAT from the received responses.
Where the test subject is the manager 330, the group 430 of
managers, or the managing entity 480, the product analysis module
240 (e.g., via the implicit association module 245) may calculate
such results from the received responses.
[0067] In operation 1118, the user interface module 220 causes the
user interface to present a priming scenario to the test subject
(e.g., to the user 152, via the display on the device 150).
Presented after a first (e.g., unprimed) IAT, the priming scenario
may cause the test subject to respond differently to a second
(e.g., primed) IAT. In the context of recommending financial
products (e.g., hedge funds), the priming scenario may include one
or more images or descriptions of financial disasters, financial
crisis, or other situations likely to evoke fear or insecurity in
the test subject regarding financial risk.
[0068] In operation 1120, the user interface module 220 causes the
user interface to present (e.g., administer) a further IAT (e.g., a
second, primed IAT) to the test subject (e.g., to the user 152, via
a display on the device 150). The user interface module 220 may
further receive responses of the test subject to this further IAT
(e.g., from the device 150). Where the test subject is the user
310, the group 410 of users, or the user entity 460, the user
analysis module 230 (e.g., via the implicit association module 235)
may calculate one or more results (e.g., primed results) of this
further IAT from the received responses. Where the test subject is
the manager 330, the group 430 of managers, or the managing entity
480, the product analysis module 240 (e.g., via the implicit
association module 245) may calculate such results from the
received responses.
[0069] In example embodiments that involve presenting one or more
IATs (e.g., as in operation 1110), performance of operation 1030
may include generating or updating a risk model (e.g., risk model
720) based on the results of the one or more IATs (e.g., unprimed
results, primed results, or both). This risk model may then be
included in the resulting psychometric profile (e.g., psychometric
profile 312). Where the test subject is the user 310, the group 410
of users, or the user entity 460, operation 1030 may be performed
by the user analysis module 230 (e.g., via the risk module 234).
Where the test subject is the manager 330, the group 430 of
managers, or the managing entity 480, operation 1030 may be
performed by the product analysis module 240 (e.g., via the risk
module 244).
[0070] In situations where the psychometric profile (e.g.,
psychometric profile 412 or 462) models a set of multiple
individual users (e.g., group 410 of users, or user entity 460),
the group module 236 within the user analysis module 230 may
aggregate respective risk models for each of the individual users
and generate or update a risk model for the entire set. Similarly,
where the psychometric profile (e.g., psychometric profile 432 or
482) models a set of multiple individual managers (e.g., group 430
of managers, or managing entity 480), the group module 246 within
the product analysis module 240 may aggregate respective risk
models for each of the individual managers and generate or update a
risk model for the set as a whole. As shown in FIG. 11, one or more
of operations 1040, 1050, 810, and 830 may then be performed as
previously described above with respect to FIGS. 8 and 10.
[0071] According to some example embodiments, the recommendation
machine 110 is configured to generate a psychometric profile (e.g.,
psychometric profile 322) as an inferred psychometric profile of a
product (e.g., product 320) or a quasi-psychometric profile of the
product. As shown in FIG. 12, the method 800 may include one or
more of operations 1210, 1220, 1230, 1240, 1250, 1260, 1270, 1280,
and 1290, prior to operation 820.
[0072] In operation 1210, the access module 210 accesses the
statistical data 610 (e.g., performance statistics) that indicate
changes in the value of a product (e.g., product 320) within the
market over time. In the context of recommending financial products
(e.g., a hedge fund), the statistical data 610 may include monthly
returns (e.g., monthly absolute returns for the hedge fund).
[0073] In operation 1220, the product analysis module 240
calculates a normalized representation of kurtosis for the product
(e.g., product 320), based on the statistical data 610 accessed in
operation 1210. This may be performed by calculating the kurtosis
and normalizing it relative to other products. For example, a
kurtosis score may be calculated and normalized between zero and
one, where zero represents the lowest kurtosis compared to other
products, and one represents the highest kurtosis compared to other
products. According to some example embodiments, the normalized
kurtosis score for a financial product represents a measure of
relative abundance in months with unusually high returns or
unusually low returns. Thus, a relatively high score (e.g., high
percentile) may indicate a risk-seeking financial product, while a
relatively low score (e.g., low percentile) may indicate a
risk-averse financial product. As used herein, the term "kurtosis"
refers to a measure of the relative population of data extrema.
Positive kurtosis indicates a data distribution with "fat tails"
(e.g., a higher population of extreme positive and negative data
points, relative to a normal distribution), in contrast to negative
kurtosis indicating a distribution with "thin tails" (e.g., a lower
population of extreme positive and negative data points, relative
to a normal distribution). The term "kurtosis" is intended to
include the fourth-moment (4th-moment) of the normal distribution
(e.g., the fourth power of the differences of each data point and
the mean of the distribution).
[0074] In operation 1230, the product analysis module 240
calculates a normalized representation of skewness for the product
(e.g., product 320), based on the statistical data 610 accessed in
operation 1210. This may be performed by calculating the skewness
and normalizing it relative to other products. For example, a skew
score may be calculated and normalized between zero and one, where
zero represents the lowest skew compared to other products, and one
represents the highest skew compared to other products. According
to some example embodiments, the normalized skewness score for a
financial product represents a measure of relative asymmetry in
monthly returns (e.g., a frequency of monthly losses compared to a
frequency of monthly gains). Thus, a relatively high score may
indicate an agile financial product that is able to react quickly
to adverse conditions and thereby limit losses, while a relatively
low score may indicate the opposite. As used herein, the term
"skewness" refers to a measure of the relative population of
positive or negative data. Positive skew indicates a data
distribution with more positive data than negative data, especially
in the "tails" of the distribution, in contrast to negative skew
indicating a distribution with a "negative tail," and relatively
fewer positive data points. The term "skewness" is intended to
include the third-moment (3rd-moment) of the normal distribution
(e.g., the third power of the differences of each data point and
the mean of the distribution).
[0075] In operation 1240, the product analysis module 240
calculates a standard deviation of a set of rolling standard
deviations, based on the statistical data 610 accessed in operation
1210, and normalizes this calculated standard deviation relative to
other products. This may be performed by calculating the standard
deviation of monthly returns within each of multiple overlapping
12-month spans of time represented in the statistical data 610.
These are the rolling standard deviations. A further standard
deviation may be calculated from these rolling standard deviations,
which may be considered as the standard deviation of the set of
rolling standard deviations. This may result in a score that
represents the degree to which the value of the product (e.g.,
product 320) is stationary over time. After normalization relative
to other products, the score represents a degree of relative
"stationarity" of the product in relation to other products. For
example, the score may be normalized between zero and one, where
zero represents the lowest degree of stationarity, and one
represents the highest degree of stationarity compared to other
products. According to some example embodiments, the normalized
stationarity score for a financial product represents a measure of
how well the financial product is able to control volatility in
monthly returns. Thus, a relatively high score may indicate less
ability to control volatility, while a relatively low score may
indicate greater ability, compared to other financial products.
[0076] In operation 1250, the product analysis module 240
calculates a normalized number of products managed by a manager
(e.g., manager 330) of the product (e.g., product 320). This may be
performed by identifying the manager from the statistical data 610
and accessing (e.g., from news or a look up table, which may be
stored in the database 115) the number of products managed by that
manager. This number may be normalized relative to other products.
For example, the normalized number may range between zero and one,
where zero indicates that the manager manages no additional
products, and one indicates that the manager manages the maximum
number of additional products compared to managers of the other
products.
[0077] In operation 1260, the product analysis module 240
calculates a normalized number of assets under management by a
manager (e.g., manager 330) of the product (e.g., product 320).
This may be performed by identifying the manager from the
statistical data 610 and accessing (e.g., from news or a look up
table, which may be stored in the database 115) the number of
assets within products that are managed by that manager. This
number may be normalized relative to other products. For example,
the normalized number may range between zero and one, where zero
indicates that the manager manages the lowest number of assets, and
one indicates that the manager manages the maximum number of assets
compared to managers of the other products.
[0078] In operation 1270, the product analysis module 240
calculates a normalized correlation of monthly returns for the
product (e.g., as represented in the statistical data 610) relative
to a published market indicator (e.g., the Standard & Poor's
500 index). This may be performed by accessing the monthly returns
from the statistical data 610, calculating their correlation to the
published market indicator, and then normalizing the correlation
relative to other products. For example, the normalized correlation
may range between zero and one, where zero represents the lowest
correlation with the published market indicator, and one represents
the highest correlation with the published market indicator,
relative to other products.
[0079] In operation 1280, the product analysis module 240 creates
or updates a psychometric profile for the product (e.g.,
psychometric profile 322 for the product 320). This may be
accomplished by creating or updating an inferred or approximated
market model (e.g., market model 710) for the product, creating or
updating an inferred or approximated risk model (e.g., risk model
720) for the product, or both. The market model, the risk model, or
both may be created or updated based on the results of one or more
of operations 1220, 1230, 1240, 1250, 1260, and 1270 (e.g., the
normalized kurtosis score, the normalized skewness score, the
standard deviation of the rolling standard deviations, the
normalized number of products managed by the manager of the
product, the normalized number of assets under management by the
manager, and the normalized correlation of monthly returns relative
to the market indicator).
[0080] The product analysis module 240 may then include (e.g.,
incorporate) the market model, the risk model, or both in the
psychometric profile (e.g., quasi-psychometric profile) for the
product (e.g., psychometric profile 322 for the product 320). In
operation 1290, the product analysis module 240 stores the
generated psychometric profile for the product in the database 115.
Accordingly, the database 115 may store this psychometric profile
for subsequent access by the access module 210 in performing
operation 820 or operation 920. As shown in FIG. 12, operation 820
may then be performed as previously described above with respect to
FIG. 8. Similarly, operation 920 may then be performed as
previously described above with respect to FIG. 9.
[0081] According to certain example embodiments, the recommendation
machine 110 may provide a recommendation of a product (e.g., as
described above with respect to operation 870 and operation 970) in
response to a request submitted by a user (e.g., user 132, via the
device 130). Examples are illustrated with respect to FIG.
13-15.
[0082] As shown in FIG. 13, the method 800 may include one or more
of operations 1310, 1340, 1342, 1344, and 1346. In operation 1310,
the user interface module 220 receives a search (e.g., search
request) submitted by the user 132 from the device 130. The
submitted search may include or otherwise indicate various criteria
for identifying one or more products (e.g., product 320) available
for recommendation. Performance of one or more of operations 810,
820, 830, and 840 may obtain (e.g., identify) a set of products
(e.g., products 320, 420, and 470) that fit the psychometric
profile (e.g., psychometric profile 312) of the user 132 and that
fit these various search criteria specified in the submitted
search. For filtering the set of products, the user interface
module 220 may cause the user interface to present one or more
parameter selection sliders (e.g., interactive parameter selection
bars) configured to specify various filtering criteria. Example
embodiments of such parameter selection sliders are illustrated and
discussed with respect to FIG. 16. In certain example embodiments,
such parameter sliders are used to specify the search criteria, the
filtering criteria, or both.
[0083] FIG. 16 is a screenshot illustrating a user interface 1600
with parameter selection sliders, according to some example
embodiments. As shown, the user interface 1600 may include a
hard-edge slider 1610, a soft-edge slider 1620, the goal slider
1630, or any suitable combination thereof. The hard-edge slider
1610 includes a parameter selection bar 1612 that ranges from a
minimum value (e.g., 0%) to a maximum value (e.g., 100%). The
parameter selection bar 1612 may include two slidable markers
(e.g., a minimum slider and a maximum slider) that are movable
along the length of the parameter selection bar 1612 to specify a
sub-range between the minimum and maximum values (e.g., a sub-range
from 27% to 58%). As implemented in some example embodiments, this
specified sub-range is absolute (e.g., "hard-edged"); all values
within the sub-range are selected (e.g., as search criteria or
filtering criteria), and all values beyond the sub-range are
omitted (e.g., from the search criteria or filtering criteria).
[0084] The soft-edge slider 1620 includes a value bar 1622 that may
similarly range from a minimum value (e.g., 0%) to a maximum value
(e.g., 3%). A parameter selection bar 1622 is shown as including
two slidable markers (e.g., a minimum slider and a maximum slider)
that are movable along the length of the parameter selection bar
1622 to specify a sub-range between a minimum and maximum values
(e.g., a sub-range from 0.5% to 1.5%). As implemented in some
example embodiments, this specified sub-range is not absolute
(e.g., not "hard-edged"), and while all values within sub-range are
selected (e.g., as search criteria or filtering criteria), some
additional values beyond the sub-range are also allowed to be
selected (e.g., as part of the same search criteria or same
filtering criteria). The selection of such additional values may be
governed by an importance bar 1624 and its slidable marker (e.g.,
an importance slider) that is movable on the importance bar 1624 to
specify an importance value (e.g., an importance score of 7 on a
scale of 1 to 10). The importance value may specify a margin for
additional values, such that additional values that fall outside
the sub-range by less than the margin are included in the
selection. For example, a high importance value may cause a
selection to be more strict (e.g., thin margin) and allow inclusion
of fewer additional values outside the sub-range, while a low
importance value may cause a selection to be less strict (e.g., fat
margin) and allow inclusion of more additional values outside the
sub-range. In certain example embodiments, the influence strength
512 for the metaphor statement 510 is specified (e.g., in operation
1010) by a test subject operating a slidable marker of an
importance bar, similar to the importance bar 1624, presented
contemporaneously with the metaphor statement 510.
[0085] The goal slider 1630 includes a parameter selection bar 1632
that may likewise range from a minimum value (e.g., 0 years) to a
maximum value (e.g., 30 years). A parameter selection bar 1632 is
shown as including a single slidable marker (e.g., a value slider)
that is movable along the length of the parameter selection bar
1632 to specify a goal value (e.g., an ideal value or a desired
value) within the total range between the minimum and maximum
values. As implemented in some example embodiments, this specified
goal value is not absolute, and while the goal value is selected
(e.g., as a search criterion or a filtering criterion), some
additional values above and below the goal value are also allowed
to be selected (e.g., as additional search criteria or additional
filtering criteria). The selection of such additional values may be
governed by an importance bar 1634 and its slidable marker (e.g.,
an importance slider) that is movable on the importance bar 1634 to
specify an importance value (e.g., an importance score of 8 on a
scale of 1 to 10). The importance value may specify a margin for
additional values, such that additional values that fall outside
the sub-range by less than the margin are included in the
selection. For example, a high importance value may cause a
selection to be more strict (e.g., narrow or small margin) and
allow inclusion of fewer additional values that differ from the
goal value (e.g., only those additional values that are very close
to the goal value), while a low importance value may cause a
selection to be less strict (e.g., wide or big margin) and allow
inclusion of more additional values that differ from the goal value
(e.g., values further above or below the goal value).
[0086] Returning the FIG. 13, in operation 1340, the user interface
module 220 causes the user interface 1600 to present one or more
parameter selection sliders for specifying various filtering
criteria that may be applied to results of the search (e.g., search
request) received in operation 1310. For example, the user
interface module 220 may cause a user interface 1600 to present the
hard-edge slider 1610, the soft-edge slider 1620, the goal slider
1630, or any suitable combination thereof
[0087] In operation 1342, the user interface module 220 receives a
selection of a range specified by the hard-edge slider 1610 (e.g.,
as specified by the user 132 via the device 130). In operation
1344, the user interface module 220 receives a selection of a range
specified by the soft-edge slider 1620, along with a corresponding
importance value (e.g., as specified by the user 132 via the device
130). In operation 1346, the user interface module 220 receives a
selection of a goal value specified by the goal slider 1630, along
with a corresponding importance value (e.g., as specified by the
user 132 via the device 130). Although operations 1342, 1344, and
1346 have been described in the context of receiving filtering
criteria, similar operations may be performed to receive the search
criteria discussed above with respect to operation 1310.
[0088] FIG. 13 additionally shows one or more of operations 810,
820, 830, 840, 850, and 870 being performed (e.g., as described
above with respect to FIG. 8). Based on filtering criteria obtained
from one or more of operations 1342, 1344, and 1346, as well as the
psychometric profile (e.g., psychometric profile 312) of the user
132, operation 850 may be performed to identify a product (e.g.,
product 320) that matches the search criteria for operation 1310,
as well as the filtering criteria. Thus, the identification of the
product may be in response to the search (e.g., search request)
submitted by the user 132 and received in operation 1310.
[0089] As shown in FIG. 14, the method 800 may include operation
1410. In operation 1410, the user interface module 220 receives a
request (e.g., match request) to identify one or more products that
match the user 132. Such a request may be submitted by the user 132
from the device 130. The submitted request may include or otherwise
indicate various additional criteria for identifying one or more
matching products (e.g., product 320). Performance of one or more
of operations 810, 820, 830, and 840 may obtain (e.g., identify) a
set of products that fit the psychometric profile of the user 132
and may also fit any additional criteria specified in the request.
For filtering the set of products, the user interface module 220
may cause the user interface to present one or more parameter
selection sliders to specify filtering criteria, as discussed above
with respect to FIG. 16. In certain example embodiments, such
parameter sliders are used to specify the additional criteria, the
filtering criteria, or both.
[0090] FIG. 14 also shows one or more of operations 810, 820, 830,
840, 860, 870, 1340, 1342, 1344, and 1346 being performed (e.g., as
described above with respect to FIGS. 8 and 13). Based on filtering
criteria obtained from one or more of operations 1342, 1344, and
1346, as well as the psychometric profile (e.g., psychometric
profile 312) of the user 132, operation 860 may be performed to
identify a product (e.g., product 320) that matches the user 132,
in addition to fulfilling the filtering criteria. Thus, the
identification of the product may be in response to the request
(e.g., match request) submitted by the user 132 and received in
operation 1410.
[0091] As shown in FIG. 15, the method 800 may include operation
1510. In operation 1510, the user interface module 220 receives a
request (e.g., alternative request) to identify one or more second
products (e.g., output products, such as products 420 and 470) as
alternatives to a first product (e.g., input product, such as
product 320). Such a request may be submitted by the user 132 from
the device 130. The submitted request may include or otherwise
indicate various additional criteria for identifying suitable
alternative products.
[0092] FIG. 15 also shows one or more of operations 1340, 1342,
1344, 1346, 810, 920, 830, 840, 950, and 970 being performed (e.g.,
as described above with respect to FIGS. 8, 9, and 13). Based on
filtering criteria obtained from one or more of operations 1342,
1344, and 1346, as well as the psychometric profile (e.g.,
psychometric profile 322) of the first product (e.g., product 320),
operation 960 may be performed to identify a second product (e.g.,
product 420) that fulfills the filtering criteria and is
sufficiently similar to the first product to be recommended as an
alternative to the first product. In some example embodiments, the
identified second product may also have a psychometric profile that
matches the psychometric profile of the user 132. Thus, the
identification of the product may be in response to the request
(e.g., alternative request) submitted by the user 132 and received
in operation 1510.
[0093] According to various example embodiments, one or more of the
methodologies described herein may facilitate assessments of
implicit and non-conscious (e.g., unconscious) psychological
understandings of financial markets, as held by financial
decision-makers. Moreover, one or more of the methodologies
described herein may facilitate determinations of non-conscious
propensities for risk-taking or safety-seeking in financial
decision-makers, as well as determinations of the influence of
situational factors (e.g., market volatility, market crisis, or
change market conditions) on such non-conscious propensities.
Hence, one or more of the methodologies described herein may
facilitate making recommendations of financial products by matching
financial decision-makers (e.g., hedge fund investors) to
investments (e.g., hedge funds), enabling prospective investors to
search and identify investments using psychometric profiles,
identifying investment alternatives based on psychometric profiles
(e.g., including quasi-psychometric profiles of such alternatives),
or any suitable combination thereof.
[0094] When these effects are considered in aggregate, one or more
of the methodologies described herein may obviate a need for
certain efforts or resources that otherwise would be involved in
making recommendations of financial products. Efforts expended by a
user in identifying obtaining such recommendations may be reduced
by one or more of the methodologies described herein. Computing
resources used by one or more machines, databases, or devices
(e.g., within the network environment 100) may similarly be
reduced. Examples of such computing resources include processor
cycles, network traffic, memory usage, data storage capacity, power
consumption, and cooling capacity.
[0095] Although various example embodiments are discussed above
with respect to recommendation of one or more financial products
(e.g., hedge funds, mutual funds, exchange-traded funds, stocks,
stock options, bonds, commodities, certificates of deposit, and
other investment instruments), the systems and methods discussed
herein may be applied to other types of products or any suitable
combination of products (e.g., goods, services, information, and
licenses). For example, the systems and methods discussed herein
may facilitate recommendation of real estate (e.g., a house, an
apartment, a vacation rental, a neighborhood, a community, a city,
a state, or a country), personnel (e.g., services of a prospective
employee, consultant, vendor, supplier, customer, manager, or team
thereof), goods (e.g., a car, a computer, or a digital camera),
recreation (e.g., an offer for leisure activity, a club membership,
a vacation, a cruise, or a holiday package), web-based services
(e.g., a messaging service, a data storage service, or a social
networking service), healthcare services (e.g., of a physician, a
clinic, a hospital, or a patient thereof), mental health services
(e.g., of a psychotherapist or a client thereof), educational
services (e.g., of a school, a college, university, a teacher, a
professor, a seminar, a training camp, or a student thereof),
hospitality services (e.g., of a hotel, a bed-and-breakfast, a
guesthouse, a hostel, a campground, or a cruise line), or any
suitable combination thereof. In addition to products available for
sale, the systems and methods discussed herein may facilitate
recommendation of any selectable thing that may be associated with
(e.g., assigned or mapped to) a psychometric profile (e.g., a
vocation, a religion, or a hobby).
[0096] FIG. 17 is a block diagram illustrating components of a
machine 1700, according to some example embodiments, able to read
instructions 1124 from a machine-readable medium 1722 (e.g., a
machine-readable storage medium, a computer-readable storage
medium, or any suitable combination thereof) and perform any one or
more of the methodologies discussed herein, in whole or in part.
Specifically, FIG. 17 shows the machine 1700 in the example form of
a computer system within which the instructions 1724 (e.g.,
software, a program, an application, an applet, an app, or other
executable code) for causing the machine 1700 to perform any one or
more of the methodologies discussed herein may be executed, in
whole or in part. In alternative embodiments, the machine 1700
operates as a standalone device or may be connected (e.g.,
networked) to other machines. In a networked deployment, the
machine 1700 may operate in the capacity of a server machine or a
client machine in a server-client network environment, or as a peer
machine in a distributed (e.g., peer-to-peer) network environment.
The machine 1700 may be a server computer, a client computer, a
personal computer (PC), a tablet computer, a laptop computer, a
netbook, a cellular telephone, a smartphone, a set-top box (STB), a
personal digital assistant (PDA), a web appliance, a network
router, a network switch, a network bridge, or any machine capable
of executing the instructions 1724, sequentially or otherwise, that
specify actions to be taken by that machine. Further, while only a
single machine is illustrated, the term "machine" shall also be
taken to include any collection of machines that individually or
jointly execute the instructions 1724 to perform all or part of any
one or more of the methodologies discussed herein.
[0097] The machine 1700 includes a processor 1702 (e.g., a central
processing unit (CPU), a graphics processing unit (GPU), a digital
signal processor (DSP), an application specific integrated circuit
(ASIC), a radio-frequency integrated circuit (RFIC), or any
suitable combination thereof), a main memory 1704, and a static
memory 1706, which are configured to communicate with each other
via a bus 1708. The processor 1702 may contain microcircuits that
are configurable, temporarily or permanently, by some or all of the
instructions 1724 such that the processor 1702 is configurable to
perform any one or more of the methodologies described herein, in
whole or in part. For example, a set of one or more microcircuits
of the processor 1702 may be configurable to execute one or more
modules (e.g., software modules) described herein.
[0098] The machine 1700 may further include a graphics display 1710
(e.g., a plasma display panel (PDP), a light emitting diode (LED)
display, a liquid crystal display (LCD), a projector, a cathode ray
tube (CRT), or any other display capable of displaying graphics or
video). The machine 1700 may also include an alphanumeric input
device 1712 (e.g., a keyboard or keypad), a cursor control device
1714 (e.g., a mouse, a touchpad, a trackball, a joystick, a motion
sensor, an eye tracking device, or other pointing instrument), a
storage unit 1716, an audio generation device 1718 (e.g., a sound
card, an amplifier, a speaker, a headphone jack, or any suitable
combination thereof), and a network interface device 1720.
[0099] The storage unit 1716 includes the machine-readable medium
1722 (e.g., a tangible and non-transitory machine-readable storage
medium) on which are stored the instructions 1724 embodying any one
or more of the methodologies or functions described herein. The
instructions 1724 may also reside, completely or at least
partially, within the main memory 1704, within the processor 1702
(e.g., within the processor's cache memory), or both, before or
during execution thereof by the machine 1700. Accordingly, the main
memory 1704 and the processor 1702 may be considered
machine-readable media (e.g., tangible and non-transitory
machine-readable media). The instructions 1724 may be transmitted
or received over the network 190 via the network interface device
1720. For example, the network interface device 1720 may
communicate the instructions 1724 using any one or more transfer
protocols (e.g., hypertext transfer protocol (HTTP)).
[0100] In some example embodiments, the machine 1700 may be a
portable computing device, such as a smart phone or tablet
computer, and have one or more additional input components 1730
(e.g., sensors or gauges). Examples of such input components 1730
include an image input component (e.g., one or more cameras), an
audio input component (e.g., a microphone), a direction input
component (e.g., a compass), a location input component (e.g., a
global positioning system (GPS) receiver), an orientation component
(e.g., a gyroscope), a motion detection component (e.g., one or
more accelerometers), an altitude detection component (e.g., an
altimeter), and a gas detection component (e.g., a gas sensor).
Inputs harvested by any one or more of these input components may
be accessible and available for use by any of modules described
herein.
[0101] As used herein, the term "memory" refers to a
machine-readable medium able to store data temporarily or
permanently and may be taken to include, but not be limited to,
random-access memory (RAM), read-only memory (ROM), buffer memory,
flash memory, and cache memory. While the machine-readable medium
1722 is shown in an example embodiment to be a single medium, the
term "machine-readable medium" should be taken to include a single
medium or multiple media (e.g., a centralized or distributed
database, or associated caches and servers) able to store
instructions. The term "machine-readable medium" shall also be
taken to include any medium, or combination of multiple media, that
is capable of storing the instructions 1724 for execution by the
machine 1700, such that the instructions 1724, when executed by one
or more processors of the machine 1700 (e.g., processor 1702),
cause the machine 1700 to perform any one or more of the
methodologies described herein, in whole or in part. Accordingly, a
"machine-readable medium" refers to a single storage apparatus or
device, as well as cloud-based storage systems or storage networks
that include multiple storage apparatus or devices. The term
"machine-readable medium" shall accordingly be taken to include,
but not be limited to, one or more tangible data repositories in
the form of a solid-state memory, an optical medium, a magnetic
medium, or any suitable combination thereof
[0102] 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.
[0103] Certain embodiments are described herein as including logic
or a number of components, modules, or mechanisms. Modules may
constitute either software modules (e.g., code embodied on a
machine-readable medium or in a transmission signal) or hardware
modules. A "hardware module" 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 modules 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 module that operates to
perform certain operations as described herein.
[0104] In some embodiments, a hardware module may be implemented
mechanically, electronically, or any suitable combination thereof.
For example, a hardware module may include dedicated circuitry or
logic that is permanently configured to perform certain operations.
For example, a hardware module may be a special-purpose processor,
such as a field programmable gate array (FPGA) or an ASIC. A
hardware module may also include programmable logic or circuitry
that is temporarily configured by software to perform certain
operations. For example, a hardware module may include software
encompassed within a general-purpose processor or other
programmable processor. It will be appreciated that the decision to
implement a hardware module 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.
[0105] Accordingly, the phrase "hardware module" 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 module" refers to a
hardware module. Considering embodiments in which hardware modules
are temporarily configured (e.g., programmed), each of the hardware
modules need not be configured or instantiated at any one instance
in time. For example, where a hardware module 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 modules) at different times.
Software may accordingly configure a processor, for example, to
constitute a particular hardware module at one instance of time and
to constitute a different hardware module at a different instance
of time.
[0106] Hardware modules can provide information to, and receive
information from, other hardware modules. Accordingly, the
described hardware modules may be regarded as being communicatively
coupled. Where multiple hardware modules 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 modules. In embodiments in which multiple hardware
modules are configured or instantiated at different times,
communications between such hardware modules may be achieved, for
example, through the storage and retrieval of information in memory
structures to which the multiple hardware modules have access. For
example, one hardware module may perform an operation and store the
output of that operation in a memory device to which it is
communicatively coupled. A further hardware module may then, at a
later time, access the memory device to retrieve and process the
stored output. Hardware modules may also initiate communications
with input or output devices, and can operate on a resource (e.g.,
a collection of information).
[0107] 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 modules that operate to perform one or more
operations or functions described herein. As used herein,
"processor-implemented module" refers to a hardware module
implemented using one or more processors.
[0108] Similarly, the methods described herein may be at least
partially processor-implemented, a processor 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
modules. 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)).
[0109] The performance of certain operations may be distributed
among the one or more processors, not only residing within a single
machine, but deployed across a number of machines. In some example
embodiments, the one or more processors or processor-implemented
modules 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 one or more processors or
processor-implemented modules may be distributed across a number of
geographic locations.
[0110] Some portions of the subject matter discussed herein may be
presented in terms of algorithms or symbolic representations of
operations on data stored as bits or binary digital signals within
a machine memory (e.g., a computer memory). Such algorithms or
symbolic representations are examples of techniques used by those
of ordinary skill in the data processing arts to convey the
substance of their work to others skilled in the art. As used
herein, an "algorithm" is a self-consistent sequence of operations
or similar processing leading to a desired result. In this context,
algorithms and operations involve physical manipulation of physical
quantities. Typically, but not necessarily, such quantities may
take the form of electrical, magnetic, or optical signals capable
of being stored, accessed, transferred, combined, compared, or
otherwise manipulated by a machine. It is convenient at times,
principally for reasons of common usage, to refer to such signals
using words such as "data," "content," "bits," "values,"
"elements," "symbols," "characters," "terms," "numbers,"
"numerals," or the like. These words, however, are merely
convenient labels and are to be associated with appropriate
physical quantities.
[0111] Unless specifically stated otherwise, discussions herein
using words such as "processing," "computing," "calculating,"
"determining," "presenting," "displaying," or the like may refer to
actions or processes of a machine (e.g., a computer) that
manipulates or transforms data represented as physical (e.g.,
electronic, magnetic, or optical) quantities within one or more
memories (e.g., volatile memory, non-volatile memory, or any
suitable combination thereof), registers, or other machine
components that receive, store, transmit, or display information.
Furthermore, unless specifically stated otherwise, the terms "a" or
"an" are herein used, as is common in patent documents, to include
one or more than one instance. Finally, as used herein, the
conjunction "or" refers to a non-exclusive "or," unless
specifically stated otherwise.
* * * * *