U.S. patent application number 16/569350 was filed with the patent office on 2020-01-02 for device for modifying product inventory.
This patent application is currently assigned to ImageWorks Interactive. The applicant listed for this patent is ImageWorks Interactive. Invention is credited to Mike Barrett, Thomas W. Becker, Gary W. Grube, Timothy W. Markison, John Moran.
Application Number | 20200005397 16/569350 |
Document ID | / |
Family ID | 49513398 |
Filed Date | 2020-01-02 |
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United States Patent
Application |
20200005397 |
Kind Code |
A1 |
Becker; Thomas W. ; et
al. |
January 2, 2020 |
DEVICE FOR MODIFYING PRODUCT INVENTORY
Abstract
An inventory modification device includes memory, an inventory
modification module, and operation modules. The memory stores limit
tables and inventory operational data. The inventory modification
module selects an inventory item to modify, a limit table regarding
the inventory item, an operational module based on an entry in the
limit table, and evaluation data. A specific task execution module
of the selected operation module executes a specific task on
inventory operational data of the inventory item to produce a
modified inventory item when an evaluation data filter of the
selected operation module indicates that analysis of the evaluation
data is favorable for modification of the inventory item via the
specific task.
Inventors: |
Becker; Thomas W.; (Willow
Springs, IL) ; Moran; John; (Winfield, IL) ;
Barrett; Mike; (Buffalo Grove, IL) ; Grube; Gary
W.; (Barrington Hills, IL) ; Markison; Timothy
W.; (Mesa, AZ) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
ImageWorks Interactive |
Park Forest |
IL |
US |
|
|
Assignee: |
ImageWorks Interactive
Park Forest
IL
|
Family ID: |
49513398 |
Appl. No.: |
16/569350 |
Filed: |
September 12, 2019 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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13868656 |
Apr 23, 2013 |
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16569350 |
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61641723 |
May 2, 2012 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G06F 16/23 20190101;
G06Q 40/06 20130101 |
International
Class: |
G06Q 40/06 20060101
G06Q040/06; G06F 16/23 20060101 G06F016/23 |
Claims
1. An inventory modification device comprises: a network interface;
memory; and a processing module operably coupled to the network
interface and the memory, wherein the processing module is operable
to: create a plurality of limit tables regarding one or more
product components; store a plurality of operations, wherein each
operation of the plurality of operations includes each of an open
operation, a close operation, and one or more component
modification operations, wherein the plurality of operations are
included in each limit table of the plurality of limit tables, and
further wherein each operation of the plurality of operations is in
one of three states: an active state, a triggered state, and an
inactive state; store a plurality of operation aspects
corresponding to each operation of the plurality of operations,
wherein each operation aspect of the plurality of operation aspects
includes identity of evaluation data to monitor, evaluation data
monitoring criteria, and identity of operational data of a
plurality of operational data for processing a product component,
of the one or more product components; select a product component
for processing; determine a purpose for processing the product
component, wherein the purpose for processing the product component
includes at least one of inventory management of the product
component, sale of the product component, use of the product
component, transfer of the product component, and monitoring
particular information regarding the product component; in response
to determining the purpose for processing the product component,
select a limit table of a plurality limit tables regarding the
product component; receive, via the network interface, the
evaluation data; identify the evaluation data, based on the
selected limit table; using the evaluation data monitoring
criteria, monitor the identified evaluation data; in response to
monitoring the identified evaluation data, determine whether an
operation of the plurality of operations is in a triggered state;
and in response to determining that an operation of the plurality
of operations is in a triggered state, execute one of the plurality
of operations on an identified operational data with respect to the
product component to modify the product component.
2. The inventory modification device of claim 1, wherein the
product component further comprises one or more manufacturing
components.
3. The inventory modification device of claim 1 further comprises:
determine a second purpose for processing the product component;
select a second limit table from the plurality limit tables
regarding the product component based on the second purpose for
processing the product component, wherein the second limit table
includes identity of second evaluation data to monitor, second
evaluation data monitoring criteria, second processing trigger
criteria, and identity of second operational data of the plurality
of operation data for processing the product component; receive,
via the network interface, second evaluation data; identify the
second evaluation data, based on the selected second limit table;
using the second evaluation data monitoring criteria, monitor the
identified second evaluation data; in response to monitoring the
second identified evaluation data, determine whether a second
operation of the plurality of operations is in a triggered state;
and in response to determining that the second operation of the
plurality of operations is in a triggered state, execute a second
one of the plurality of operations on the identified operational
data with respect to the product component to modify the product
component to produce a second modified product component.
4. The inventory modification device of claim 3 further comprises:
when the second operation of the plurality of operations is in a
triggered state, and before executing the second one of the
plurality of operations, determine whether to continue, stop, or
modify the determining whether the second operation of the
plurality of operations is in a triggered state; and in response to
determining that the determination is to continue: when the second
operation of the plurality of operations is in a triggered state,
execute the identified operational data with respect to second
modified tangible asset to still further modify the second modified
product component.
5. The inventory modification device of claim 4 further comprises:
in response to determining to modify the product component:
determine an updated purpose for processing the product component;
select another limit table of a plurality limit tables regarding
the product component, based on the updated purpose for processing
the product component, wherein the selected another limit table
includes identity of updated evaluation data to monitor, updated
evaluation data monitoring criteria, updated processing trigger
criteria, and identity of updated operational data of the plurality
of operation data for processing the second modified product
component; receive, via the network interface, updated identified
evaluation data; using the updated evaluation data monitoring
criteria, monitor the updated identified evaluation data; in
response to monitoring the updated identified evaluation data,
determine when the updated processing trigger criteria is met; and
when the updated processing trigger criteria is met, execute the
identified operational data with respect to a second modified
product component to yet further modify the second modified product
component.
6. The inventory modification device of claim 1, wherein the
evaluation data monitoring criteria comprises: a trigger indicator
that indicates a commencement of monitoring the identified
evaluation data; and a detrigger indicator that indicates ending of
the monitoring of the identified evaluation data.
7. The--inventory modification device of claim 1, wherein the
triggered state of the operation of the plurality of operations
further comprises: an activate indicator that indicates a
commencement of the executing the identified operational data; and
a deactivate indicator that indicates ending the executing of the
identified operational data.
8. A device comprises: an interface; and an operational processing
module that includes: an evaluation data filter; filtered data
analysis module; and a specific task execution module; wherein the
operational processing module is operable to: receive, via the
interface, selected evaluation data; receive, via the interface,
performance criteria; receive, via the interface, operational data
regarding inventory being modified; receive, via the interface,
evaluation criteria; filter, by the evaluation data filter, the
selected evaluation data based on the evaluation criteria to
produce filtered data; determine, by the filtered data analysis
module, whether to activate the specific task execution module
based on an analysis of the filtered data in view of the
performance criteria; and execute, by the specific task execution
module, a specific task on the operational data to produce a result
for modifying the inventory when the filtered data analysis module
activates the specific task execution module.
9. The device of claim 8, wherein the inventory comprises at least
one of: tangible property, financial capital, intangible assets,
intelligence information, a rented asset, and a disposable
asset.
10. The device of claim 8, wherein the filtered data analysis
module comprises: a trigger/detrigger module operable to: trigger
the specific task execution module when analysis of the evaluation
data is favorable in view of one or more trigger indicators;
detrigger the specific task execution module when analysis of the
evaluation data is favorable in view of one or more detrigger
indicators; and an activate/deactivate module; and activate the
specific task execution module when the analysis of the evaluation
data is favorable in view of one or more activate indicators; and
deactivate the specific task execution module when the analysis of
the evaluation data is favorable in view of one or more deactivate
indicators.
11. The device of claim 8, wherein the specific task comprises one
of: a logic function, a mathematical function, an algorithm, and an
operational instruction.
12. The device of claim 8, wherein the operational processing
module is further operable to: send the result to an inventory
modification module, which modifies the inventory based on the
result.
13. The device of claim 8, wherein the interface comprises at least
one of: a WLAN interface; a WAN interface; a LAN interface; and a
local computer hardware interface.
Description
CROSS REFERENCE TO RELATED PATENTS
[0001] The present U.S. Utility Patent Application claims priority
pursuant to 35 U.S.C. .sctn. 120, to U.S. Utility patent
application Ser. No. 13/868,656, entitled "DEVICE FOR MODIFYING
VARIOUS TYPES OF ASSETS," filed Apr. 23, 2013, which claims
priority pursuant to 35 USC .sctn. 119(e) to U.S. Provisional
Application No. 61/641,723, entitled "ASSET MODIFICATION
COMMUNICATION SYSTEM AND COMPONENTS THEREOF", filed May 2, 2012,
both of which are hereby incorporated herein by reference in their
entirety and made part of the present U.S. Utility Patent
Application for all purposes.
STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT
[0002] NOT APPLICABLE
INCORPORATION-BY-REFERENCE OF MATERIAL SUBMITTED ON A COMPACT
DISC
[0003] NOT APPLICABLE
BACKGROUND OF THE INVENTION
Technical Field of the Invention
[0004] This invention relates generally to communication systems
and more particularly to asset evaluation and modification within
the communication system.
Description of Related Art
[0005] Communication systems are known to allow data to be from one
or more devices within a communication system to one or more other
devices. The data may be raw data (e.g., created by a first
communication device and communicated through the communication
system unaltered), encrypted data, compressed data, processed data
(e.g., data created by a first communication device is processed
(e.g., manipulated, calculated, operand of a calculation, encoded,
encrypted, compiled, etc.) by another device within the
communication system as the data is communicated through the
communication system), etc. The data may be video data, audio data,
text data, graphics data, image data, and/or a combination
thereof.
[0006] Almost every business, if not every business, uses data and
communicates data with its customers, suppliers, employees,
contractors, etc. The data may be advertisements to customers,
purchase orders to suppliers, invoices to customers, accounting
information, business evaluation information, inventory monitoring
information, day trading information, market analysis information,
etc. Depending on the type of business, a business may utilize
large and expensive computer enterprise systems to manage its data.
For a small business or for an individual, it may use one or more
personal computers and one or more software applications to manage
its data. Whether an individual, a small business, or a large
business, managing data is an ever increasing challenge as the
amount and communication of digital data is increasing rapidly.
BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWING(S)
[0007] FIG. 1 is a schematic block diagram of an embodiment of a
data communication system in accordance with the present
invention;
[0008] FIG. 1A is a schematic block diagram of an embodiment of a
user and/or service provider device in accordance with the present
invention;
[0009] FIG. 1B is a schematic block diagram of an embodiment a
computer readable storage medium in accordance with the present
invention.
[0010] FIG. 2 is a schematic block diagram of an embodiment of an
inventory modification module in accordance with the present
invention;
[0011] FIG. 3 is a diagram of an example of modifying inventory
based on evaluation data in accordance with the present
invention;
[0012] FIG. 3A is a diagram of an example of modifying inventory
based on evaluation data in accordance with the present
invention;
[0013] FIG. 4 is a schematic block diagram of an example of
inventory modification module modifying an asset in accordance with
the present invention;
[0014] FIG. 5 is a logic diagram of a method for modifying
inventory in accordance with the present invention;
[0015] FIG. 5A is a logic diagram of a method for modifying
inventory in accordance with the present invention;
[0016] FIG. 5B is a logic diagram of a method for modifying
inventory in accordance with the present invention;
[0017] FIG. 6 is a schematic block diagram of an example of
inventory modification operations in accordance with the present
invention;
[0018] FIG. 7 is a schematic block diagram of an embodiment of
inventory modification operation in accordance with the present
invention;
[0019] FIG. 8 is a schematic block diagram of another embodiment of
inventory modification operation in accordance with the present
invention;
[0020] FIG. 8A is a schematic block diagram of an embodiment of
evaluation data filter in accordance with the present
invention;
[0021] FIG. 9 is a diagram of an example of an operation set limit
table in accordance with the present invention;
[0022] FIGS. 10A-10C are diagrams of an example of asset
modification in accordance with the present invention;
[0023] FIG. 11 is a schematic block diagram of another example of
an inventory modification module modifying an asset in accordance
with the present invention;
[0024] FIG. 12 is a schematic block diagram of another example of
an inventory modification module modifying an asset in accordance
with the present invention;
[0025] FIG. 13 is a schematic block diagram of another example of
an inventory modification module modifying an asset in accordance
with the present invention;
[0026] FIG. 14 is a schematic block diagram of another example of
an inventory modification module modifying an asset in accordance
with the present invention;
[0027] FIG. 15 is a schematic block diagram of another example of
an inventory modification module modifying an asset in accordance
with the present invention;
[0028] FIG. 16 is a schematic block diagram of another example of
an inventory modification module modifying an asset in accordance
with the present invention;
[0029] FIG. 17 is a schematic block diagram of another example of
an inventory modification module modifying an asset in accordance
with the present invention;
[0030] FIG. 17A is a schematic block diagram of another example of
an inventory modification module modifying an asset in accordance
with the present invention;
[0031] FIGS. 18-20 are a logic diagram of another method for
modifying an inventory in accordance with the present
invention;
[0032] FIG. 21 is a schematic block diagram of an example of an
inventory modification module selecting a limit table for asset
modification in accordance with the present invention;
[0033] FIG. 22 is a diagram of an example of inventory factors in
accordance with the present invention;
[0034] FIG. 23 is a diagram of an example of attributes in
accordance with the present invention;
[0035] FIG. 24 is a logic diagram of a method of an inventory
management function in accordance with the present invention;
[0036] FIG. 25 is a logic diagram of a method of another inventory
management function in accordance with the present invention;
[0037] FIG. 26 is a schematic block diagram of another example of
an inventory modification module modifying an asset in accordance
with the present invention;
[0038] FIG. 27 is a schematic block diagram of another example of
an inventory modification module modifying an asset in accordance
with the present invention;
[0039] FIG. 28 is a schematic block diagram of another example of
an inventory modification module modifying an asset in accordance
with the present invention;
[0040] FIG. 29 is a schematic block diagram of another example of
an inventory modification module modifying an asset in accordance
with the present invention;
[0041] FIG. 30 is a schematic block diagram of another embodiment
of an inventory modification module in accordance with the present
invention;
[0042] FIGS. 31-33 are a logic diagram of another method for
modifying an inventory in accordance with the present
invention;
[0043] FIG. 34 is a diagram of another example of an operation set
limit table in accordance with the present invention;
[0044] FIG. 35 is a diagram of another example of a generalized
operation set limit table in accordance with the present
invention;
[0045] FIG. 36 is a diagram of an example of operation sequencing
based on an operation set limit table in accordance with the
present invention;
[0046] FIG. 37 is a schematic block diagram of another example of
an inventory modification module modifying inventory in accordance
with the present invention;
[0047] FIG. 38 is a schematic block diagram of another example of
an inventory modification module modifying an asset in accordance
with the present invention;
[0048] FIG. 39 is a schematic block diagram of another example of
an inventory modification module modifying inventory in accordance
with the present invention;
[0049] FIG. 40 is a schematic block diagram of another example of
an inventory modification module modifying inventory in accordance
with the present invention;
[0050] FIGS. 41-42 are a diagram of another example of an operation
set limit table in accordance with the present invention;
[0051] FIG. 43 is a diagram of an example of modifying an asset
based on the limit table of FIGS. 41-42 in accordance with the
present invention;
[0052] FIG. 44 is a diagram of another example of modifying an
asset based on the limit table of FIGS. 41-42 in accordance with
the present invention;
[0053] FIG. 45 is a diagram of another example of modifying
inventory based on the limit table of FIGS. 41-42 in accordance
with the present invention;
[0054] FIG. 46 is a diagram of another example of modifying
inventory based on the limit table of FIGS. 41-42 in accordance
with the present invention;
[0055] FIG. 47 is a diagram of another example of modifying
inventory based on the limit table of FIGS. 41-42 in accordance
with the present invention;
[0056] FIG. 48 is a diagram of an example of interoperations of
multiple operation set limit tables in accordance with the present
invention;
[0057] FIG. 49 is a diagram of an example of operation of an
operation set limit table in accordance with the present
invention;
[0058] FIG. 50 is a diagram of another example of operation of an
operation set limit table in accordance with the present
invention;
[0059] FIG. 51 is a diagram of another example of operation of an
operation set limit table in accordance with the present
invention;
[0060] FIG. 52 is a diagram of another example of operation of an
operation set limit table in accordance with the present
invention;
[0061] FIG. 53 is a diagram of another example of an operation set
limit table in accordance with the present invention;
[0062] FIG. 54 is a diagram of an example of building an operation
set limit table in accordance with the present invention;
[0063] FIG. 55 is a diagram of an example of historical evaluation
data for building an operation set limit table in accordance with
the present invention;
[0064] FIG. 56 is a schematic block diagram of an embodiment of an
evaluation filter of an inventory modification module in accordance
with the present invention;
[0065] FIG. 57 is a diagram of an example of a reference pattern in
accordance with the present invention;
[0066] FIG. 58 is a diagram of an example of a reference pattern in
accordance with the present invention;
[0067] FIG. 59 is a schematic block diagram of an example of
operation of an evaluation filter in accordance with the present
invention;
[0068] FIG. 60 is a diagram of an example output of an evaluation
filter in accordance with the present invention;
[0069] FIGS. 61-63 are a logic diagram of a method for building a
limit table in accordance with the present invention;
[0070] FIG. 64 is a logic diagram of another method for building a
limit table in accordance with the present invention;
[0071] FIG. 65 is a schematic block diagram of an example of an
assembly line of building a product in accordance with the present
invention;
[0072] FIG. 66 is a diagram of an example of limit tables for
inventory management of an assembly line of building a product in
accordance with the present invention;
[0073] FIG. 67 is a diagram of an example of limit tables for
inventory management of an assembly line of building a product in
accordance with the present invention;
[0074] FIG. 68 is a diagram of an example of inventory management
of an assembly line of building a product in accordance with the
present invention;
[0075] FIG. 69 is a diagram of an example of graphical user
interface in accordance with the present invention;
DETAILED DESCRIPTION OF THE INVENTION
[0076] FIG. 1 is a schematic block diagram of an embodiment of a
data communication system that includes a plurality of user devices
10-12, a plurality of service provider devices 14-16, a plurality
of evaluation content sources 18-20, and one or more networks 22
(e.g., one or more local area networks
[0077] (LAN), one or more wireless LANs (WLAN), one or more wide
area networks (WAN), the Internet, etc.). Each of the user devices
10-12 includes one or more network interfaces 24 (e.g., LAN, WLAN,
WAN, Internet, etc.), one or more processing modules 26, and memory
28. Each of the service provider devices 14-16 includes one or more
network interfaces 24, one or more processing modules 26, and
memory 28. The one or more processing modules 26 of the user
devices 10-12 and/or the service provider devices 14-16 implement
one or more asset modification modules 30.
[0078] In an example of operation, a user device 10-12 has a
portfolio of assets 32, a collection of operation set limit tables
34, and a pool of operations 36. The assets 32 may be tangible
property (e.g., real estate, inventory, energy (e.g., gas,
electric, etc.), financial capital (e.g., money, stock, bonds,
precious metals, etc.), etc.), intangible assets (e.g., patents,
copyrights, trademarks, good will, trade secrets, etc.),
intelligence information (e.g., personal data, person of interest
data, weather data, sports data, traffic data, competitor
information, etc.), a rented asset, and/or a disposable asset
(e.g., tickets). Each limit table 34 includes entries, where an
entry includes identity of one or more evaluation data to monitor,
trigger indicators, de-trigger indicators, activation indicators,
de-activations indicators, identity of one or more operations of
the pool of operations 36, operational data indicators, status
information, etc. The pool of operations 36 includes operations
regarding modification of an asset, where modification includes
increase amount of an asset, decrease amount of an asset, dispose
of some or all of an asset, use some or all of the asset, transfer
some or all of the asset, assign some or all of the asset, adjust
sales prices of an asset, adjust anticipated purchase price of an
asset, adjust purchasing procedures of an asset, buy more of an
asset, sell some or all of an asset, etc. Other operations of the
pool of operations 36 include compiling evaluation data, evaluating
evaluation data, trend recognition, pattern recognition, data
extrapolation, etc.
[0079] For a specific asset, the asset modification module 30 uses
an operation set limit table to manage the modification of the
asset. The operation set limit table may be the only one for this
asset or may be selected from a plurality of operation set limit
tables 34 for this asset. The selection of an operation set limit
table may be automatic based on user preferences (e.g., a
conservative approach, an aggressive approach, modification
thresholds for the asset (e.g., lower limit on how the asset might
decrease), evaluation data of preference, how the asset is
modifying during a given period of time, etc.) or selected via a
user input.
[0080] As a specific example, assume that the asset is a stock. In
this specific example, the limit table includes entries on when to
buy more of the stock and how much to buy based on various
conditions, trends, patterns, limits, and/or other factors of the
evaluation data (e.g., line price data, candlestick data, average
position price, simple moving average, exponential moving average,
Bollinger bands, money flow, MACD, parabolic SAR, rate of change,
relative strength, slow Stochastic, fast Stochastic, volume,
volume+moving average, William % R, etc.). The limit table also
includes entries on when to sell some or all of the stock based on
various conditions, trends, patterns, limits, and/or other factors
of the evaluation data. The limit table further includes entries on
when to "open" the stock for modification and when to "close" the
stock modification.
[0081] As another specific example, assume that the asset is a
component of a production product. In this specific example, the
selected limit table generally includes entries to manage
maintaining inventory of the component at a desired level (e.g.,
just-in-time, having a surplus, etc.) for manufacturing the product
based on various conditions, trends, patterns, limits, and/or other
factors of the evaluation data (e.g., component pricing, component
availability, political issues effecting availability of the
component, sub-component material pricing, sub-component material
availability, shipping, import/export factors, assembly
requirements, labor issues, weather, supply-demand information,
market information for the product, etc.). For instance, the limit
table may include an entry to determine when to buy the component,
an entry to determine the per purchase quantity of the component,
an entry to determine the purchase price of the component, an entry
to select one or more vendors, an entry to determine a desired
inventory level, etc.
[0082] As another specific example, assume that the asset is season
tickets to a sporting event. In this specific example, the selected
limit table generally includes entries to manage, on a per event
basis, the sale of tickets, use of the tickets, donation of the
tickets, and/or transfer of the tickets to family or friends based
on various conditions, trends, patterns, limits, and/or other
factors of the evaluation data (e.g., season ticket holder's
schedule, weather, opponent's fan following, current success of
home team, standings, time of season, playoff scenarios, season
ticket holder's desired level of attendance, other ticket sales
quantity for an event, other ticket sales pricing for the event,
etc.). For instance, the limit table may include an entry to set
the sales price above face value, an entry to set the sales price
below face value, an entry to determine which events to sell the
tickets, an entry to determine which events to use the tickets,
etc.
[0083] As another specific example, assume that the asset is
intelligence data regarding a particular person. In this specific
example, the selected limit table generally includes entries to
manage data regarding an individual based on various conditions,
trends, patterns, limits, and/or other factors of the evaluation
data (e.g., person's financial information, person's press
clippings, person's family information, person's education
information, person's employment information, number of inventions,
number of published papers, purchase history, etc.). For instance,
the limit table may include an entry to determine the person's
shopping preferences, an entry to determine the person's likes, an
entry to determine the person's dislikes, an entry to determine the
person's profession success, an entry to determine the person's
potential for employment viability, etc.
[0084] In another example of operation, a service provider device
14-16 functions similarly to a user device 10-12, but does so on
behalf of clients. As such, for each client, the service provider
devices 14-16 functions to manage the client's assets in similar
manner as the user device 10-12. In this regard, for each client,
the service provider device 14-16 stores a portfolio of assets 32,
a collection of operation set limit tables 34, and a pool of
operations 36.
[0085] FIG. 1A is a schematic block diagram of an embodiment of a
device 11 (e.g., user device 10 and/or service provider device 14)
that includes one or more network interfaces 24, one or more
processing modules 26, and memory 28. The processing module(s) 26
implement one or more asset modification modules 30. The memory 28
stores a plurality of limit tables 38-40, operational data 42-44
regarding assets (e.g., tangible assets, intangible assets,
financial capital, intelligence information, rented assets,
disposable assets, etc.), and operation instruction sets 46-48.
[0086] In an example of operation, the processing module 26 selects
an asset to modify from a portfolio of assets. The processing
module 26 identifies which of the plurality of limit tables 38-40
correspond to the selected asset and selects one or more of the
limit tables. The processing module interprets the appropriate
limit table, or tables, to identify time-varying and time-sensitive
data regarding the asset and one or more corresponding operation
sets. The processing module receives the time-varying and
time-sensitive data from a local area network or a wide area
network through the network interface 24. The time-varying and
time-sensitive data is data from one or more sources (e.g.,
subscription based data providers, free data providers, vendors,
suppliers, weather, government, etc.) that varies over time and,
for the purposes of a current asset modification, is only useful
for a given period of time (e.g., minutes, days, weeks, months,
years).
[0087] The processing module evaluates the time-varying and
time-sensitive data based on evaluation criteria contained in the
one or more limit tables 38-40. When the evaluation is favorable,
the one or more operation instruction sets 46-48 are triggered
(e.g., one or more operations is selected, is retrieved from
memory, is loaded into the processing module, is readied for
execution, etc.). With an operation triggered, the processing
module further evaluates the time-varying and time-sensitive data
based on correlated evaluation criteria of the limit tables 38-40
to determine whether to activate the operation instruction sets
46-48. When activated, the processing module executes the operation
on the operational data 42-44 to modify the asset.
[0088] FIG. 1B is a schematic block diagram of an embodiment of a
computer readable storage medium 50 coupled to device 11 (e.g., the
user device 10 and/or the service provider device 14). The computer
readable storage medium includes a plurality of storage sections 52
that store data and/or operational instructions. A storage section
52 includes one or more byte-addressable, word-addressable,
multiple word-addressable, or other sized-addressable lines such
that the storage section is capable of storing bytes to Giga-bytes
of data and/or operational instructions. The computer readable
storage medium 50 may be one or more memory devices, where a memory
device is a disk, a memory of a download source, a flash memory, a
USB drive, an external hard drive, a memory of a computer, etc.
[0089] In an example of operation, the computer readable storage
medium 50, or a portion thereof, contains five storage sections 52;
each of which storing operational instructions that, when executed
by the processing module 26, causes the processing module to
perform one or more functions, tasks, sub-routines, algorithms,
etc. A first storage section stores operational instructions 54
that cause the processing module 26 to obtain one or more limit
tables regarding an asset. A second storage section stores
operational instructions 54 that cause the processing module 26 to
identify time-varying and time-sensitive data from the one or more
limit tables. A third storage section stores operational
instructions 54 that cause the processing module 26 to analyze the
time-varying and time-sensitive data based on evaluation criteria
from the one or more limit tables. A fourth storage section 52
stores operational instructions 54 that, when the analyzing the
time-varying and time-sensitive data is favorable, causes the
processing module 26 to trigger one or more operations of a set of
operations corresponding to a particular criteria.
[0090] With one or more operations triggered, a fifth storage
section 54 stores operational instructions 54 that causes the
processing module 26 to further analyze the time-varying and
time-sensitive data based on correlated evaluation criteria of the
evaluation criteria. When the further analyzing the time-varying
and time-sensitive data in view of the correlated particular
criteria is favorable, the processing module 26 activates the one
or more operations to execute a corresponding function on
operational data corresponding to the asset such that the asset is
modified.
[0091] FIG. 2 is a schematic block diagram of a functional
embodiment of an asset modification module 30. The asset
modification module 30 manages a portfolio of assets 32 using
operation sets 56. For instance, for a particular asset (e.g.,
tangible property, financial capital, intangible assets,
intelligence information, a rented asset, and/or a disposable
asset), the asset modification module 30 selects a limit table from
one or more limit tables for the asset. The limit table identifies
an operation set 56 (e.g., a set of operations used to modify the
asset in accordance with limits, conditions, parameters, etc.,
listed in the limit table. Note that one or more operations for
modifying an asset may be in one or more operation sets 56.
[0092] As a specific example, assume that asset #1 is the selected
asset and asset #1 operation (op) set #1 is identified via
selection of the corresponding limit table 1-1. A first operation
of the set of operations may be to "open" asset #1 for
modification. This may be done at the selection of limit table 1-1
or it may be an operation within the limit table based on a
condition of one or more evaluation data. Once the asset is open,
an operation is evoked based on an entry of the limit table based
on conditions of one or more evaluation data. For example, an entry
may evoke an operation to buy a particular quantity of asset #1
(e.g., component of a manufactured product) at a best current price
when a particular inventory level is reached. Another entry may
evoke an operation to buy a particular quantity of the asset when a
particular price is reached. Another entry may evoke an operation
to determine when to buy a particular quantity when the evaluation
data indicates that the supply of the asset may drop and the price
may go up. Another entry may evoke an operation to track use of the
asset and/or potential future use of the asset. Another entry may
evoke an operation to close the asset when a certain level of
inventory is reached. These are but a few examples of operations
that may be evoked by entries in a limit table to manage the
inventory of a component or of any other limit table.
[0093] When the asset is closed, the asset modification module
returns it to the portfolio of assets (i.e., is no longer actively
in a modification mode of the asset). The asset modification module
30 may modify one asset at a time or a plurality of assets
concurrently. If the asset modification module 30 is modifying
multiple assets, each asset is treated separately with respect to
selection of a limit table and the corresponding operations as well
as execution of the corresponding operations based on the limit
table.
[0094] FIG. 3 is a diagram of an example of modifying an asset
based on evaluation data 58 (D1, D2, and D3). Each of the
evaluation data 58 is time varying and time sensitive, wherein the
duration of variation may vary by the second, by the minute, by the
hour, by the day, by the week, by the month, and/or by the year.
For example, an evaluation data 58 may be a stock price, which
varies at fractions of a second. As another example, an evaluation
data 58 may be weather conditions, which varies by the minute, by
the hour, by the day, by the week, by the month, and by the year,
where its variation rate of interest depends how the weather data
is being evaluated. For instance, if the weather data is being used
as factor in determining the long-term availability of a resource
(e.g., natural, element, component, etc.), then the variation rate
may be months and/or years. Alternatively, if the weather data is
being used as a factor in determining whether to sell tickets for
tonight's baseball game, then the variation rate may be minutes
and/or hours. Other examples of evaluation data 58 are almost
endless and correspond to data of interest regarding a particular
asset.
[0095] In the diagram, the horizontal axis represents time 60
(which may be measured in seconds, minutes, hours, days, weeks,
months, and/or years), the positive vertical axis represents
evaluation data variations 62, and the negative vertical axis
represents operation data of the asset 64. Operation data of the
asset 64 may include quantity, price, data operands (e.g., opening
price, baseline values, opening quantities, consumption quantities,
etc.), data allotment (e.g., a percentage of the current asset to
expose to modification), asset information, etc.
[0096] In an example of operation, once the asset is open 76 for
modification 78, the evaluation data 58 is monitored to detect a
triggering condition 66 (e.g., price increase to a certain level,
price decrease to a certain level, supply-demand information at a
certain level, combination of evaluation data trend and/or pattern
detection, etc.). When a triggering condition 66 is met, the asset
modification module then determines whether an activate condition
68 is met, which may be at the same threshold as the triggering
condition 66 or at a different threshold. When the activation
condition 68 is met, the corresponding operation 70 is activated
until a deactivation condition 72 is met or the modification 78 of
the asset is closed 80.
[0097] In this example, operation 2 (O2) is triggered by the
opening of the asset and is activated when evaluation data D2 and
D3 become available. With operation 2 active, evaluation data D3 is
monitored for a particular condition to cause operation 2 to
execute (e.g., buy when price is below $x.00). Alternatively,
operation 2 may be continually executed based on evaluation data D3
(e.g., compare D3 with stored data to determine a variance). As
operation 2 is executed, the operation data of the asset 64 is
modified. Operation 2 is deactivated when evaluation data D3 is no
longer available and is de-triggered 74 when the modification 78 of
the asset is closed 80.
[0098] As is further included in this example, when evaluation data
D1 begins to rise, operation 1 is triggered 66 and remains in the
triggered state (e.g., grey shaded area) until it rises further. At
this point, operation 1 is activated 68 and is executed when a
particular condition of evaluation data D1 is met or is continually
executed using evaluation data D1. Operation 3 is triggered 66,
activated 68, deactivated 72, and de-triggered 74 based on factors,
trends, and/or patterns of D1-D3.
[0099] FIG. 3A is a diagram of another example of modifying an
asset based on evaluation data 58 (D1, D2, and D3). Each of the
evaluation data 58 is time varying and time sensitive. In the
diagram, the horizontal axis represents time 60 (which may be
measured in seconds, minutes, hours, days, weeks, months, and/or
years), the positive vertical axis represents evaluation data
variations 62, and the negative vertical axis represents operation
data of the asset 64.
[0100] In an example of operation, once the asset is open 76 for
modification 78, the evaluation data 58 is monitored to detect a
triggering condition 66. When a triggering condition 66 is met, the
asset modification module then determines whether an activate
condition 68 is met, which may be at the same threshold as the
triggering condition 66 or at a different threshold. When the
activation condition 68 is met, the corresponding operation 70 is
activated and executed. Once executed, the operation 70 is
deactivated 72 and placed in the triggered state again. When the
activation condition 68 is again met, the operation 70 is activated
and executed; once executed it is again deactivated 72 and placed
in the triggered state. This cycle of activate, execute, and
re-trigger continues until a de-trigger condition 74 is met. For
example, if the asset is a stock and the evaluation data 58
continues to show favorable conditions for purchasing additional
shares of the stock, then the operation of purchasing shares will
be repeated each time the activation condition 68 is met.
[0101] FIG. 4 is a schematic block diagram of an example of an
asset modification module 30 modifying an asset 86. In this
example, the asset modification module 86 selects the asset 86 to
modify and a limit table 88. In addition, the asset modification
module 30 receives evaluation data 90, which may be received from
one or more evaluation data content sources. The limit table 88
identifies a corresponding operation set 92 (e.g., the operations
identified in the limit table 88).
[0102] In an example of operation, the asset module 30 monitors the
evaluation data 90 in accordance with entries of the limit table 88
for a condition, pattern, trend, peak value, valley value, etc. to
be met to trigger an operation. When an operation is triggered, the
asset modification module 30 retrieves it from the corresponding
operation set 92 and sets it in the triggered state. When an
activation condition, pattern, trend, peak value, valley value,
etc. is met, the asset modification module 30 changes the operation
to an active state and executes the operation when appropriate
based on the evaluation data analysis. This process continues until
the asset modification module 30 closes modification of the asset
and outputs a modified asset 94.
[0103] FIG. 5 is a logic diagram of a method for modifying an asset
that is executable by the asset modification module of a device 11.
The method begins at step 96 the module selects an asset from a
portfolio of assets. Recall that an asset may be tangible property
(e.g., real estate, inventory, energy (e.g., gas, electric, etc.),
financial capital (e.g., money, stock, bonds, precious metals,
etc.), etc.), intangible assets (e.g., patents, copyrights,
trademarks, good will, trade secrets, etc.), intelligence
information (e.g., personal data, person of interest data, weather
data, sports data, traffic data, competitor information, etc.), a
rented asset, and/or a disposable asset (e.g., tickets). The
selection of an asset may be based on factors associated with the
asset, where the factors includes a type of asset, modification
timing (e.g., time of day, inventory depletion, etc.), evaluation
data sources of interest availability, evaluation data analysis
(e.g., pattern mapping, trend detection, value thresholds,
comparative analysis, etc.).
[0104] The method continues at step 98 where the module selects or
creates an operation set limit table for the asset 98 to be
modified. For example, a plurality of operation set limit tables
may exist for a particular asset. Each of the operation set limit
tables is developed based on different attributes (e.g., risk
level, evaluation data relevancy, asset modification philosophy,
reliability level (e.g., proven, unproven, works some times, etc.),
favorable evaluation data patterns and/or trends, performance
information, etc.). Accordingly, for this example, the operation
set limit table is selected based on its attributes aligning with
the desires of the user. Alternatively, the asset modification
module may create an operation set limit table, which is discussed
with reference to one or more subsequent figures.
[0105] The method continues at step 100 where the module analyzes
time-varying and time-sensitive data based on evaluation criteria
of interest identified by the operation set limit table (examples
are discussed with reference to one or more subsequent figures).
The method continues at step 102 where the module determines
whether analysis of the time-varying and time-sensitive data in
view of a particular criteria of the evaluation criteria is
favorable for triggering an operation. If not, the process
continues at step 104 where the module determines whether
modification of the asset is being closed (e.g., user initiated,
evaluation data determined, limit table indicator, etc.). If the
modification is being closed, the method continues at step 106
where the asset modification module outputs a modified asset. Note
that the asset may not be modified if the modification process was
closed before an operation was executed. If the modification is not
being closed, the method repeats at the analyze evaluation data
step 100.
[0106] If the time-varying and time-sensitive data in view of a
particular criteria of the evaluation criteria is favorable for
triggering the operation, the operation is triggered and the method
continues at step 108 where the module further analyzes the
time-varying and time-sensitive data based on correlated evaluation
criteria of the evaluation criteria to determine whether the
operation is to be activated. Note that the evaluation criteria
provides one or more of a desired trend, a desired pattern, a
desired slope, a desired value, a desired indicator, a desired
threshold, a desired deviation over time, etc. Further note that
the particular criteria and the correlated criteria may be the same
desired evaluation criteria such that an operation is triggered and
activated substantially simultaneously or may be different desired
evaluation criteria such that the operation is triggered at one
level of the desired evaluation criteria and activated at another
level.
[0107] If the operation is not to be activated, the method
continues at step 110 where the module determines whether the
evaluation data analysis indicates that the operation is to be
detriggered. If not, the method repeats in a loop for this
operation between the activating step 108 and the detriggering 110.
The method also branches back to the analyze evaluation data step
100 for another operation. Accordingly, the module may be executing
the method of FIG. 5 for multiple operations in view of the same or
different evaluation data and/or criteria to modify the selected
asset.
[0108] If the operation is activated, the method continues at step
111 where the module executes the operation in accordance with
operation data indicators of the operation set limit table. The
method continues at step 112 where the module determines whether
the operation is to be deactivated. The operation may be
deactivated upon execution of the operation or based upon an
indicator of the evaluation data analysis indicating deactivation
of the operation. If not, the method repeats at the operation
execution step 111. The method also branches back to the analyze
evaluation data step 100 for another operation. If the operation is
being deactivated, the method continues at the detrigger
determination step 110. Note that when the operation is
deactivated, it is placed back in the triggered state unless
otherwise indicated in the operation set limit table.
[0109] If an indicator of the evaluation data analysis indicates
detriggering the operation, the method continues at step 114 where
the module determines whether the modification of the asset is to
be closed. If the modification is to be closed, the method
continues at step 116 where the asset modification module outputs a
modified asset. If the modification is not being closed, the method
repeats, for this operation, at the trigger determination step 102
and repeats at the evaluation data step 100 for another
operation.
[0110] FIG. 5A is a logic diagram of a method for modifying an
asset that is executable by the asset modification module within
the processing module of the user and/or service provider device.
The method begins at step 118 where the module obtains, by
selecting from stored limit tables or creating a new one) one or
more limit tables regarding an asset to be modified. The method
continues at step 120 where the asset modification module
identifies time-varying and time-sensitive data from the one or
more limit tables regarding the asset to be modified 120. The
time-varying and time-sensitive data may be obtained from one or
more wide area network (WAN), local area network (LAN) data sources
(e.g., news, weather, sports, credit tracking, criminal records,
financial information, real estate information, data collection,
marketing information, sales information, forecasting, vendor web
pages, governmental, etc.) via network interface(s) on a free or
subscription basis.
[0111] The method continues at step 122 where the asset
modification module analyzes the time-varying and time-sensitive
data based on evaluation criteria from the one or more limit
tables. In general, the analysis of the data is based on evaluation
criteria that includes, but is not limited to, detecting
predictable patterns, trends, factors, values, slopes, deviations,
indicators, thresholds, comparative analysis, etc. of the current
evaluation data. For example, the analysis is based on a particular
one or more of the evaluation criteria such as a particular pattern
mapping, a particular trend, a particular value, a particular
threshold, a particular comparative analysis, a particular slope, a
particular word content, a particular phrase content, a particular
time stamp, a particular data of interest identifiers, etc. as is
described in greater detail with reference to one or more of FIGS.
7, 21, and 24. The analysis may further include a level of
favorability (or unfavorability), which indicates how closely the
detected patterns, trends, factors, values, etc. match past
patterns, trends, factor values, etc. and the likelihood they will
continue to match. The level of favorability (or unfavorability) is
a factor in determining the predictability that the asset will be
modified in a desired manner. For example, when the analysis yields
patterns, trends, etc. that don't particularly match the
predictable patterns, trends, etc., the favorability may be
indeterminate as is described in greater detail with reference to
FIG. 54.
[0112] The method branches at step 124 based on whether analysis of
step 122 is favorable. When the analysis is unfavorable, the method
continues at step 125 where the asset modification module
determines whether to close the modification of the asset. If the
asset modification is to be closed, the method ends for
modification of the selected asset. If the modification is not to
be closed, the method repeats at step 120 where the asset
modification module identifies new evaluation data (e.g., the
time-varying and time sensitive data) or uses the previously
selected evaluation data.
[0113] When the analysis of step 124 is favorable, the method
continues at step 126 where the asset modification module triggers
one or more operations of a set of operations, which is identified
in the limit table(s). The method continues at step 128 where the
asset modification module further analyzing the evaluation data
based on correlated evaluation criteria. In an example, the
correlated evaluation criteria correlates to the particular
evaluation criteria, but a different desired level, value,
threshold, etc.
[0114] The method branches at step 130 depending on whether the
analysis of step 128 is favorable. When the analysis is
unfavorable, the method continues at step 131 where the asset
modification module determines whether to detrigger the operation.
The asset modification module may detrigger the operation when the
analysis of the evaluation data compares unfavorably to the
particular evaluation criteria, a time period elapses, as an
auto-response to an unfavorable correlated evaluation criteria
analysis, etc. If the operation is not to be detrigger, the method
repeats at step 128. If the operation is to be detrigger, the asset
modification module detriggers it and the method repeats at step
125.
[0115] If the further analysis based on the correlated evaluation
criteria is favorable, the method continues at step 132 where the
asset modification module activates the operation is activated. The
method continues at step 133 where the asset modification module
executes the operation on the operational data corresponding to the
asset to produce an intermediate modified asset. The execution of
the operation may occur substantially immediately after activation,
after a predetermined period of, after the evaluation data remains
in a favorable relation to the correlated evaluation criteria for a
given period of time, etc. In another example, the particular
criteria and the correlated criteria may be the same. In this case,
step 126, where the operation is triggered, and step 132, where the
operation is activated and executed are the same time; thus, the
operation is triggered, activated, and executed in the same
step.
[0116] The method continues at step 134 where the asset
modification module determines whether to deactivate the operation
and place it in a triggered state. The asset modification module
may deactivate the operation as an auto-response to executing the
operation, when the analysis of the evaluation data in view of the
correlated evaluation criteria is unfavorable, after elapse of a
time period, after the operation has been executed a given number
of times, etc. If the operation is to be deactivated, the method
continues at step 136 where the module deactivates the operation,
places it back in the triggered state, and the method repeats at
step 128. If the operation is not to be deactivated, the method
repeats at step 133. Note that the repeating of execution of the
operation may be substantially continual or may include some delay
or hysteresis. Further note that the repeating of the execution of
the operation may produce a cumulative modification of the asset or
a superseding modification of the asset.
[0117] FIG. 5B is a logic diagram of a method for modifying an
asset that is executable by the asset modification module within
the processing module of the user or service provider device. The
method begins at step 138 where the asset modification module
selects an asset from a plurality of assets. The method continues
at step 140 where the asset modification module identifies one or
more limit tables of a plurality of limit tables corresponding to
the selected asset. The method continues at step 142 where the
module retrieves, via the network interface module, time-varying
and time-sensitive data (e.g., evaluation data) based on
information from the one or more limit tables.
[0118] The method continues at step 144 where the module identifies
one or more operations of a plurality of operations based on
evaluation of the time-varying and time-sensitive data as indicated
in the one or more limit tables. The method continues at step 146
where the module retrieves operation data corresponding to the
selected asset. The operational data may include the selected
asset, an asset identifier (ID), information regarding the selected
asset (e.g., quantity, value, use rate, etc.), and/or other data
regarding the asset.
[0119] The method continues at step 148 where the module triggers
the one or more operations based on further evaluation of the
time-varying and time-sensitive data. The method continues at step
150 where the module executes the one or more operations on the
operation data for the selected asset based on further evaluation
of the time-varying and time-sensitive data.
[0120] FIG. 6 is a schematic block diagram of an example of a set
of operations for modifying an asset. Each of the operations may be
an operation module (e.g., software and/or hardware) that includes
a plurality of inputs and one or more outputs. The inputs include
operation data indicator, or indicators, (ODI), evaluation data
indicator, or indicators, (EDI), trigger indicator, or indicators,
(TI), de-trigger indicator, or indicators, (DT), activate for
execution indicator, or indicators, (EI), pause or deactivate
indicator, or indicators, (PI), and/or data input (Din). The output
includes a resultant output (Rout).
[0121] For an operation, the operation data indicator(s) provide
indications regarding operation data of the asset. Such indicators
may include an operation data identifier (ID), a data allotment,
and one or more data operands. For example, the operation data ID
identifies the asset, or data regarding the asset. As a more
specific example, the operation data ID may identify a stock for
trading, a person for collecting information about him or her,
employment data of a person, a part number of a component, a region
for weather information, sporting event tickets, etc.
[0122] The data allotment provides operational limits on the
operation data being modified. For example, the data allotment may
indicate how much of the identified stock is to be executed upon by
the operation, what years to obtain information regarding a person,
certain employers of a person for employment data, a quantity of an
identified part, an amount of time for collecting weather data of a
region, dates for the sporting event tickets, etc. Note that the
data allotment may be predetermined via the operation set limit
table or it may be a calculation of the operation and/or another
operation.
[0123] The one or more data operands, if included, provide operand
information regarding the operation data. For example, a data
operand may be an initial value of the operation data, baseline
data for the operation data, data conditions (e.g., limits,
thresholds, rounding preferences, etc.) regarding the operation
data, data formatting (e.g., units, number of decimal places, date
format, font, etc.), and/or calculation parameters (e.g., function
to perform to determine baseline data, initial data, etc.). As a
more specific example, the data operand may indicate currency of
the identified stock, date format for the years to obtain
information regarding a person, certain employers of a person for
employment data, quantity units (e.g., per piece, per dozen, per
lots of 100, etc.) of an identified part, a start time for
collecting weather data of the region, quantity of tickets for the
specific dates regarding the sporting event, etc.
[0124] Continuing with inputs for the operation, each of the
evaluation data indicator(s) (EDI) includes an evaluation data
identifier (ID) and one or more evaluation data factors. The
evaluation data ID identifies one or more evaluation data sources
of interest for the particular operation. For example, the
evaluation data of interest for a stock may be one or more of line
price data, candlestick data, average position price, simple moving
average, exponential moving average, Bollinger bands, money flow,
MACD, parabolic SAR, rate of change, relative strength, slow
Stochastic, fast Stochastic, volume, volume+moving average, William
% R, etc. As a further example, the evaluation data of interest for
information regarding a person may be specific newspapers, specific
magazines, credit rating information, criminal history, financial
information, published patent applications, articles authorized by
the person, etc. As another example, the evaluation data of
interest for employment data of a person may be tax returns,
information regarding the employer, personnel files, etc. As yet
another example, the evaluation data of interest for weather of a
particular region may be local forecasts, national weather service
advisories, video data, informant information, etc.
[0125] An evaluation data factor for evaluation data of interest
includes one or more of a time frame for the evaluation data (e.g.,
data of May 12, 2012), source filtering information, evaluation
data manipulation requirements, etc. For example, the source
filtering information may be used to weight information from one
source as more relevant than from another source, to filter out
information from a particular source, to pass only information from
one or more selected authors, etc. As another example, the
evaluation data manipulation requirements provide information
regarding changing format of the evaluation data (e.g., language
translation, font, spacing, size, paragraph structure, tabular
form, spreadsheet form, etc.); performing a function on the
evaluation data (e.g., a mathematical function (e.g., an average
function, an RMS function, etc.), graphing, scaling, compressing,
etc.); performing a function of the evaluation data with other
evaluation data (e.g., comparing, adding, subtracting, multiplying,
dividing, averaging, statistical analysis, etc.).
[0126] Continuing with inputs for the operation, the trigger
indicator(s) indicate when the operation is to be triggered (e.g.,
awaken and ready for execution) based on analysis of the identified
evaluation data. For example, the trigger indicator may indicate
that, when the evaluation data reaches a certain value, the
operation is to be triggered. As another example, the trigger
indicator may indicate that, when the evaluation data exhibits a
certain pattern and/or trend, the operation is to be triggered. As
yet another example, the trigger indicator may indicate that, when
a combination of evaluation data exhibits a certain pattern and/or
trend, the operation is to be triggered. The de-trigger
indicator(s) indicate when the operation is to be detriggered
(e.g., put into a sleep mode or disabled).
[0127] Continuing with inputs for the operation, the activate for
execution indicator(s) indicate when a triggered operation is to be
activated (e.g., enabled for execution) based on analysis of the
identified evaluation data. For example, the activate for execution
indicator may indicate that, when the evaluation data reaches a
certain value, the triggered operation is to be activated. As
another example, the activate for execution indicator may indicate
that, when the evaluation data exhibits a certain pattern and/or
trend, the triggered operation is to be activated. As yet another
example, the activate for execution indicator may indicate that,
when a combination evaluation data exhibits a certain pattern
and/or trend, the operation is to be activated. The de-activate for
execution indicator(s) indicate when the operation is to be
deactivated (e.g., put back into the triggered mode).
[0128] FIG. 7 is a schematic block diagram of an embodiment of
asset modification operation module 152 that includes a data filter
154, a wake-up/sleep module 156, an execute activate/deactivate
module 158, and an execution module 160. The operation module 152
may be implemented as a software module executable by a processing
module, may be implemented as a hard coded module, and/or may be
implemented as a firmware module (e.g., a combination of software
and hardware).
[0129] In an example of operation, the data filter 154 filters one
or more evaluation data 162 based on one or more evaluation data
indicators 164 to produce one or more filtered evaluation data
outputs. Since the evaluation data 162 is time-varying and
time-sensitive, the one or more filtered evaluation data outputs
are a continuous stream of data, a continuous stream of data
samples, and/or a combination thereof. Note that the evaluation
data 162 may include an output of one or more other operations.
Further note that the evaluation data 162 may include the output
resultant 166 of the operation module 152.
[0130] The wake-up/sleep module 156 (which could also be called a
trigger/detrigger module) receives the one or more filtered
evaluation data outputs and analyzes them in view of the one or
more trigger indicators 168. When the analysis is favorable, the
wake-up/sleep module 156 wakes up, or triggers, the operation
module 152. A favorable analysis may result by detecting a trigger
signal; by detecting the filtered evaluation data includes a data
aspect (e.g., value, trend, pattern, slope, word content, phrase
content, time stamp, data of interest identifier, etc.) that
compares favorably to a trigger indicator 168; by detecting a
desired result of another operation; and/or by detecting one or
more other factors within the filtered evaluation data.
[0131] When the operation is triggered (e.g., awake), the execute
activate/deactivate module 158 is engaged to analyze the one or
more filtered evaluation data outputs in view of one or more
execute (or activate) indicators 170. When the analysis is
favorable, the execute activate/deactivate module 158 enables, or
activates, the operation module 152. A favorable analysis may
result by detecting an activate signal; by detecting the wake up
signal (e.g., an execute indicator is the same as a trigger
indicator); by detecting the filtered evaluation data includes a
data aspect (e.g., value, trend, pattern, slope, word content,
phrase content, time stamp, data of interest identifier, etc.) that
compares favorably to an execute (or activate) indicator; by
detecting a desired result of another operation; and/or by
detecting one or more other factors within the filtered evaluation
data.
[0132] With the operation active, the execution module 160 is
enabled to perform its operation on the operation data 174 and/or
on the evaluation data 162 in view of the operation data indicators
172. For example, the operation may be a logic function, a
mathematical function, an algorithm, and/or an operational
instruction. As a more specific example, the operation is an
algorithm to purchase stock, which is identified by the operation
data 174 and/or the operation data indicators 172. As another more
specific example, the operation is an algorithm to sell sporting
event tickets, which are identified by the operation data and/or
the operation data indicators. As yet another more specific
example, the operation is an algorithm to update a file on a person
with newly found evaluation data. As a further more specific
example, the operation is an algorithm to determine when to place a
purchase order for a component of a manufactured product. As an
even further more specific example, the operation is an algorithm
to generate an alarm when the national weather service has issued a
severe storm warning for a region. As a still further more specific
example, the operation is a function to change the formatting of a
selected evaluation data.
[0133] When the operation is active, the execute
activate/deactivate module 158 is analyzing the one or more
filtered evaluation data outputs in view of one or more pause (or
deactivate) indicators 176 to determine whether to deactivate, or
disable, the operation module 152. When the analysis is favorable,
the execute activate/deactivate module 158 disables, or
deactivates, the operation module 152, which places the operation
module 152 back in the triggered state. A favorable analysis may
result by detecting a deactivation signal; by detecting completion
of the execution module performing its operation; by detecting the
filtered evaluation data includes a data aspect (e.g., value,
trend, pattern, slope, word content, phrase content, time stamp,
data of interest identifier, etc.) that compares favorably to a
pause (or deactivate) indicator 176; by detecting a desired result
of another operation; and/or by detecting one or more other factors
within the filtered evaluation data.
[0134] With the operation module 152 back in the triggered state,
the execute activate/deactivate module 158 analyzes the one or more
filtered evaluation data outputs in view of one or more execute (or
activate) indicators 170 to determine if the operation module 152
should be activated. Also, the wake-up/sleep module 156 analyzes
the one or more filtered evaluation data outputs in view of the one
or more detrigger indicators 178. When the analysis is favorable,
the wake-up/sleep module 156 places the operation module 152 in a
sleep, or detriggered state. A favorable analysis may result by
detecting a detrigger signal; by detecting the filtered evaluation
data includes a data aspect (e.g., value, trend, pattern, slope,
word content, phrase content, time stamp, data of interest
identifier, etc.) that compares favorably to a detrigger indicator
178; by detecting a desired result of another operation; and/or by
detecting one or more other factors within the filtered evaluation
data.
[0135] FIG. 8 is a schematic block diagram of another embodiment of
asset modification operation module 152 that includes a data filter
154, a wake-up/sleep module 156, an execute activate/deactivate
module 158, and a plurality of execution modules 160. The operation
module 152 may be implemented as a software module executable by a
processing module, may be implemented as a hard coded module,
and/or may be implemented as a firmware module (e.g., a combination
of software and hardware).
[0136] In an example of operation, the data filter 154 and the
wake-sleep module 156 function as discussed with reference to FIG.
7. The execute activate/deactivate module 158 functions similarly
to the execute activate/deactivate module 158 of FIG. 7 with the
additional function of selecting one or more of the plurality of
execution modules 160. The execute activate/deactivate module 158
selects an execution module 160, or multiple execution modules 160,
in accordance with information within the execute (or activate)
indicators 170. For example, if the indicators indicate purchasing
a stock, the execution module 160 that performs the algorithm to
purchase a stock is selected. If, however, the indicators indicate
selling a stock, the execution module 160 that performs the
algorithm to sell a stock is selected.
[0137] FIG. 8A is a schematic block diagram of an embodiment of
evaluation data filter 154 that includes an indicator processing
module 180, a plurality of data manipulation modules 182-184, a
plurality of recognition filters 186-188, and an output module 190.
The output module 190 includes a plurality of functional blocks
192-198 and a programmable switching network 200-204. The
functional blocks include, but are not limited to, an analyzer 192,
a comparator 194, a compiler 196, a data processing module 198,
etc. The programmable switching network includes an input switch
module 200, a plurality of switches (SW) 202, and an output switch
module 204. Note that the evaluation filter 154 may be implemented
as a software module executable by a processing module, may be
implemented as a hard coded module, and/or may be implemented as a
firmware module (e.g., a combination of software and hardware).
[0138] In an example of operation, the indicator processing module
180 receives the evaluation data indicator(s) 206 and may further
receive configuration information for a corresponding limit table.
From the evaluation data indicator(s) 206 and/or the configuration
information, the indicatory processing module generates control
signals 208. For instance, the indicator processing module 180
generates control signals 206 to enable one or more of the data
manipulation modules 182-184 and to configure, if needed, the
enable data manipulation module(s)182-184. For example, an
evaluation data indicator 206 identifies a particular evaluation
data 210 and, based on the identity, the indicator processing
module 180 assigns a data manipulation module 182-184 to the
particular evaluation data 210. In addition, the indicator
processing module 180 may generate a configuration control signal
208 to configure the data manipulation module 182-184 to compress,
scale, buffer, transform format, etc. of the particular evaluation
data 210. Alternatively, the indicator processing module 180 may
generate a control signal 206 to bypass a data manipulation module
182-184 such that evaluation data 210 is provided directly to a
recognition filter 186-188.
[0139] The indicator processing module 180 also generates control
signals 208 to enable one or more of the recognition filters
186-188 and may further generate control signals 208 to configure
filtering of an enabled recognition filter 186-188. For example, at
some of the recognition filters 186-188 have different and fixed
filtering functions. An example of fixed filtering functions
includes, for a particular publication as the evaluation data 210,
passing articles regarding a particular subject, passing articles
written by a particular author, passing articles written in a
particular time frame, etc. Another example of fixed filter
functions includes, for a particular evaluation data 210, passing
the evaluation data 210 during a particular time window (e.g., from
9 AM-4 PM eastern time). A further example of fixed function
functions includes a first filter function of passing data
regarding a particular subject (e.g., a component for a
manufactured product, information regarding a person, weather,
traffic, a sporting event, etc.) and a second filter function of
passing a subset of data based on one or more of the source of the
data, timeliness of the data (e.g., if not current, which is a
relative term, the data is filtered out), a particular geographic
region, etc.
[0140] The indicator processing module 180 further generates
control signals 208 to configure the output module 190. For
example, the output module 190 may be configured to output filtered
evaluation data without further processing. As another example, one
or more of the functional blocks 192-198 of the output module 190
may be enabled to process filtered evaluation data. As a specific
example, the analyzer 192 may be enabled to analyze the filtered
evaluation data to extract, highlight, etc. certain aspects of it.
As another specific example, the comparator 194 may be enabled to
compare two or more filtered evaluation data for redundancy,
relevancy, timeliness, etc. and to output one of the compared
evaluation data. As a further specific example, the compiler 196
may be enabled to compile two or more filtered evaluation data into
compiled evaluation data. As a further specific example, the data
processing module 198 may be enabled and configured to further
process the evaluation data. Such further processing may include
compression, scaling, format transformation, combining multiple
evaluation data into a combined evaluation data (e.g., multiple
weather reports from different sources into a single weather
report), sort evaluation data, de-duplicate evaluation data,
prioritize evaluation data, etc.
[0141] As yet another example of configuring the output module 190,
multiple functional blocks 192-198 may be enabled such that an
output of one functional block provides an input to another
functional block. As a further example of configuring the output
module 190, a functional block may be configured to provide a loop
function for a certain number of loops and/or until a condition is
met (e.g., the output of a functional block is fed back as the
input of the functional block). Note that the input switch module
200-204 includes sufficient buffering to temporarily store filtered
evaluation data.
[0142] FIG. 9 is a diagram of an example of an operation set limit
table 212 that includes a plurality of columns and one or more
rows. The columns correspond to particular aspects of the operation
set limit table 212 such as sequence (ordering) 214, operation
identifier (ID) 216, one or more trigger indicators 218, one or
more detrigger indicators 220, evaluation data indicators 224,
execute (or activate) indicators 226, pause (or deactivate)
indicators 228, operational data indicators 230, current operation
status 232, and execution module selection 234. Note that an
operation limit table may include more or less columns (i.e.,
aspects). For example, the execution module selection 234 may be
omitted. As another example, the sequence column 214 may be
omitted. As yet another example, a column or aspect may be added to
indicate configuration information for the evaluation data
filter.
[0143] Each row of the operation set limit table 212 corresponds to
an identified operation and its operating conditions. For example,
a first row below the header includes an entry in the sequence
(ordering) field 214 of 0(1,x) and an entry in the operation ID 216
field of "A", which identifies operation module A. In this example,
the 0 represents that operation A is an initializing operation to
begin modifying a particular asset. For example, operation A may be
detecting an initialize asset modification signal; may be detecting
a certain condition of evaluation data, etc. The (1,x) indicates
the next sequence numbers that the asset modification process may
transition to after successful execution of an operation having a
sequence number of 0. In this example, operations having a sequence
number of 1 or a sequence number of x are the next possible
operations of the asset modification process.
[0144] Continuing with the example for operation "A", the remaining
fields includes entries for triggering indicator(s) 218,
detriggering indicator(s) 220, evaluation data indicators 224
(which includes evaluation data ID 236 and may further include
evaluation data factors 238), execute (or activate) indicator(s)
226, pause (or deactivate) indicator(s) 228, operational data
indicators 230 (which includes operational data ID 240, data
allotment 242, and/or data operands 244), current operation status
232, and execution module selection 234. The current operation
status 232 includes whether the operation is waiting, in execution,
completed, etc. The execution module selection 234 is used to
select an execution module of the operation module if it includes
more than one execution module.
[0145] The second row of the table includes an entry in the
sequence (ordering) field 214 of 0 (1,x) and an entry in the
operation ID field of "B", which identifies operation module B. In
this example, the 0 represents that operation B is an initializing
operation to begin modifying a particular asset. In this example,
operations A and B are each initializing operations and either one
of the operations may be executed to transition the asset
modification process to the next sequence. The remaining rows have
information regarding other operations for the asset modification
process, with one of them being a closing operation of the asset
modification process.
[0146] FIGS. 10A-10C are diagrams of an example of asset
modification. FIG. 10A illustrates the first two columns of an
operation set limit table (i.e., sequence (ordering) 214 and
operation ID 216); FIG. 10B illustrates the sequence transitions
for operation set limit table; and FIG. 10C illustrates the states
of the operations for the sequencing of the operation set limit
table. In this example, the operation set limit table includes x
number of sequences 246 and identifies Z operations. At the opening
of the asset modification process of the operation set limit table,
it starts in sequence state 0 and ends in sequence state x.
[0147] In the sequence 0 state, operations A and B are executable
250 (i.e., can be triggered, activated, and/or executed),
operations C, D, and Z are triggerable 248 (e.g., if the asset
modification process transitions out of sequence 0, these are the
operations that may be executable), and operations E-N are inactive
252 (e.g., are not executable when the asset modification process
transitions out of sequence 0). When operation A or B is
successfully executed, the asset modification is process is in a
transitional state (e.g., it can transition to sequence 1 or to
sequence x). In this transitional state, operations C, D, and Z
become executable 250.
[0148] When a triggering condition for operation C or D occurs, the
asset modification process transitions from sequence 0 to sequence
1. In this sequence state 246, operations C and D are executable
250, operations E, F, G, and H are triggerable 248, and operations
A, B, and I-Z are inactive 252. The asset modification process
remains in sequence state 1 as long as operation C or D is
triggered and can execute operation C or D as often as indicated by
the other field entries for the operation as set in the operation
set limit table (e.g., the various indicators). When operations C
and D become detriggered, the asset modification process is again
in a transitional state. From sequence 1, the asset modification
process may be transition to sequence 2 or to sequence 3 (as
indicated by the parenthetical 2, 3 in the sequence ordering column
214 in rows having a sequence number of 1).
[0149] The asset modification process will transition to sequence 2
if operations E, F, and/or G become triggered and will transition
to sequence 3 if operation H becomes triggered. If the asset
modification process transitions to sequence 2, operations E, F,
and G are executable 250, operations E, F, G, I, J, M, and N are
triggerable 248, and operations A-D, H, K, L, and O-Z are inactive
252. The asset modification process remains in sequence state 2
until operations E, F, and G are detriggered in accordance with a
detrigger indicator (which may result for a triggering of an
operation in a next sequence state). Once detriggered, the asset
modification process is again in the transition state and may
transition back into sequence state 2, transition to sequence state
4, or transition to sequence state 6 (e.g., as indicated by the
parenthetical 2, 4, 6 in the sequence ordering column 214).
[0150] If the asset modification process transitions from sequence
1 to sequence 3, operation H is executable 250, operations H, J, K,
L, and I are triggerable 248, and operations A-G, M-Z are inactive
252. The asset modification process remains in sequence state 3
until operation H is detriggered in accordance with a detrigger
indicator. Once detriggered, the asset modification process is
again in the transition state and may transition back into sequence
state 3, transition to sequence state 4, or transition to sequence
state 5 (e.g., as indicated by the parenthetical 2, 4, 5 in the
sequence ordering column 214).
[0151] If the asset modification process transitions to sequence 4
from either sequence 2 or 3, operations I and J are executable 250,
operations C, D, K, and L are triggerable 248, and operations A, B,
E-G, and O-Z are inactive 252. The asset modification process
remains in sequence state 4 until operations I and J are
detriggered in accordance with a detrigger indicator. Once
detriggered, the asset modification process is again in the
transition state and may transition to sequence state 1 or
transition to sequence state 5 (e.g., as indicated by the
parenthetical 1, 5 in the sequence ordering column 214).
[0152] If the asset modification process transitions to sequence 5
from either sequence 3 or 4, operations K and L are executable 250,
operations C, D, and Z are triggerable 248, and operations A, B,
E-J, and M-Z are inactive 252. The asset modification process
remains in sequence state 5 until operations K and L are
detriggered in accordance with a detrigger indicator. Once
detriggered, the asset modification process is again in the
transition state and may transition to sequence state 1 or
transition to sequence state z (e.g., as indicated by the
parenthetical 1, z in the sequence ordering column 214).
[0153] If the asset modification process transitions to sequence 6
from sequence 2, operations M and N are executable 250, operations
K and L are triggerable 248, and operations A-J and O-Z are
inactive 252. The asset modification process remains in sequence
state 6 until operations M and N are detriggered in accordance with
a detrigger indicator. Once detriggered, the asset modification
process is again in the transition state and may transition to
sequence state 5 (e.g., as indicated by the parenthetical 5 in the
sequence ordering column 214).
[0154] As an example, assume that the asset being modified is
season tickets to a sporting event. Various operations for
modifying season tickets include, but are not limited to, opening
season ticket asset modification process, buy tickets for a
particular sporting event, sell tickets for a particular sporting
event, keep the tickets for a particular sporting event,
determining a selling price, determining a purchasing price,
determining a selling quantity, determining a purchasing quantity,
closing the season ticket asset modification process, etc.
[0155] For this example, sequence state 0 may correspond to opening
the season ticket asset modification process, which may be done by
detecting an opening signal (e.g., operation A) or by detecting a
trigger and execute condition for operation B (e.g., current date
is 3 weeks prior to sporting event). When the season ticket asset
modification process is opened, it is in a transition state waiting
for a trigger condition to occur, which may indicate to keep the
tickets for the particular sporting event, buy more tickets to the
sporting event, or sell tickets to the sporting event. If a trigger
condition occurs indicating keep the tickets (e.g., in town, good
opponent, etc.), the asset modification process for this particular
sporting event is closed, but other particular sporting events may
still be open.
[0156] If a trigger condition indicates buying more tickets (e.g.,
family or friends in town), the asset modification process
transitions to a sequence for purchasing additional tickets. This
sequence of purchasing additional tickets may include several
sequences (e.g., determining whether adjacent seats are available
for purchase, determining an acceptable price, determining a
quantity of extra tickets, purchasing the extra tickets, etc.). The
sequence of purchasing additional tickets may take a turn if
adjacent seats are not available. In this instance, a sequence for
purchasing a group of best available tickets may be evoked and, if
successful, then a sequence is evoked to sell the season ticket
holder's tickets for the particular sporting event.
[0157] If a trigger condition indicates selling the tickets (e.g.,
out of town, event sold out, (opportunity to sell at greater than
face value), etc.), the asset modification process transitions to a
sequence, or series of sequences to sell the tickets. The
operations of the sequence or series of sequences include
determining a number of tickets to sell, a selling price (which may
change the closer to the event), fund distribution, etc.
[0158] FIG. 11 is a schematic block diagram of another example of
an asset modification module 30 modifying an asset. In this
example, the asset modification module 30 selects one of the assets
254 to manage and retrieves the operational data 264 regarding the
asset in accordance with a selection process (e.g., user selection,
automated determination process, default selection, etc.). The
operation data 264 of an asset includes an asset ID, the asset,
and/or information regarding the asset (e.g., quantity, value, use
rate, etc.).
[0159] Once the asset modification module 30 selects an asset 254
for modification, it selects an operation set limit table 266 from
a plurality of limit tables 256. In this example, asset 1 has limit
tables 1_1 through 1_.alpha. available and the asset modification
module 30 selects one of them based on attributes and/or factors of
the limit tables (e.g., risk levels, reliability, etc.) in
accordance with user preferences or a calculated preference.
[0160] The asset modification module 30 accesses the selected limit
table to retrieve one or more rows of information 268 (e.g., a row
of information corresponds to information regarding an operation,
which may be in a particular sequence order). The asset
modification module 30 interprets the information 270 to identify
one or more evaluation data 272 and retrieves the corresponding
evaluation data 280. The asset modification module 30 further
interprets the information to identify indicators 274 (e.g.,
trigger, detrigger, activate, deactivate, etc.). The asset
modification module 30 also interprets the information to identify,
and retrieve, one or more operations 278 from a pool of operations
258.
[0161] Having retrieved indicators and the operation(s), the asset
modification module 30 analyzes the retrieved evaluation data 260,
which is data that is time varying and time sensitive and may
further be streaming data, in light of the indicators. When a
trigger indicator is met, the operation 258 is triggered and the
status of the operation within the limit table 256 is updated
accordingly 276. When an activate indicator is met, the operation
258 is activated for execution and the status of the operation
within the limit table 256 is updated accordingly 276. The asset
modification module 30 executes an activated operation 282 in
accordance with the indicators of the limit table 256 to produce a
partial modification resultant.
[0162] The asset modification module 30 continues to access the
limit table 266, identify operation data 270, identify evaluation
data 272, retrieve indicators 274, identify operations 284, and
execute the operations 282 in accordance with the identifiers. Once
the modification of the asset is closed, the asset modification
module 30 outputs an asset modification result 262.
[0163] FIG. 12 is a schematic block diagram of another example of
an asset modification module 30 modifying an asset 254 (e.g.
creating the asset or adding to the asset 286). In this example,
the asset modification module 30 selects one of the assets 254 to
create, or add to, and uses this information to select an operation
set limit table 266 from a plurality of limit tables 256.
[0164] The asset modification module 30 accesses the selected limit
table 268 to retrieve one or more rows of information 270 (e.g., a
row of information corresponds to information regarding an
operation, which may be in a particular sequence order). The asset
modification module 30 interprets the information to identify one
or more evaluation data 272 and retrieves the corresponding
evaluation data 280. The asset modification module 30 further
interprets the information to identify indicators 274 (e.g.,
trigger, detrigger, activate, deactivate, etc.). The asset
modification module 30 also interprets the information to identify
284, and retrieve 278, one or more operations from a pool of
operations 258.
[0165] Having retrieved indicators and the operation(s) 258, the
asset modification module 30 analyzes the retrieved evaluation data
260. When a trigger indicator is met, the operation 258 is
triggered and the status of the operation within the limit table
256 is updated accordingly 276. When an activate indicator is met,
the operation 258 is activated to create and/or add to the asset
254 and the status of the operation within the limit table 256 is
updated accordingly 276.
[0166] The asset modification module 30 continues to access the
limit table 256, identify operation data 270, identify evaluation
data 272, retrieve indicators 274, identify operations 284, and
execute the operations 282 in accordance with the identifiers to
create and/or add to the asset. Once the modification of the asset
is closed, the asset modification module 30 outputs an asset
modification result 262 (e.g., the generated or updated asset).
[0167] FIG. 13 is a schematic block diagram of another example of
an asset modification module 30 modifying an asset. In this
example, the asset modification module 30 selects one of the assets
254 to manage and retrieves the operational data 264 regarding the
asset in accordance with a selection process. The asset
modification module 30 also selects 290 an operation set 288 with
included limit table from a plurality of operation sets with
included limit tables. In this example, an operation set 288 has
its own limit table, as such the operation set (e.g., set of
operations that may be performed) is paired with a limit table.
[0168] The asset modification module 30 accesses the selected limit
table 268 to retrieve one or more rows of information 270 (e.g., a
row of information corresponds to information regarding an
operation, which may be in a particular sequence order). The asset
modification module 30 interprets the information to identify one
or more evaluation data 272 and retrieves 280 the corresponding
evaluation data 260. The asset modification module 30 further
interprets the information to identify indicators 274 (e.g.,
trigger, detrigger, activate, deactivate, etc.). The asset
modification module 30 also interprets the information to identify
one or more operations from the operation set 288.
[0169] Having retrieved indicators and the operation(s), the asset
modification module 30 analyzes the retrieved evaluation data 260
in light of the indicators. When a trigger indicator is met, the
operation is triggered and the status of the operation within the
limit table is updated accordingly 276. When an activate indicator
is met, the operation is activated for execution and the status of
the operation within the limit table is updated accordingly 276.
The asset modification module 30 executes an activated operation
282 in accordance with the indicators of the limit table to produce
a partial modification resultant.
[0170] The asset modification module 30 continues to access the
limit table, identify operation data 270, identify evaluation data
272, retrieve indicators 274, identify operations, and execute the
operations 282 in accordance with the identifiers. Once the
modification of the asset is closed, the asset modification module
30 outputs an asset modification result 262.
[0171] FIG. 14 is a schematic block diagram of another example of
an asset modification module 30 modifying an asset (e.g. creating
the asset or adding to the asset 286). In this example, the asset
modification module 30 selects one of the assets 254 to create or
add to and uses this information to select an operation set and
corresponding limit table 290 from a plurality of operation sets
and corresponding limit tables 288.
[0172] The asset modification module 30 accesses the selected limit
table 268 to retrieve one or more rows of information 270 (e.g., a
row of information corresponds to information regarding an
operation, which may be in a particular sequence order). The asset
modification module interprets the information to identify one or
more evaluation data 272 and retrieves the corresponding evaluation
data 280. The asset modification module 30 further interprets the
information to identify indicators 274 (e.g., trigger, detrigger,
activate, deactivate, etc.). The asset modification module 30 also
interprets the information to identify one or more operations from
the set of operations 288.
[0173] Having retrieved indicators and the operation(s), the asset
modification module 30 analyzes the retrieved evaluation data. When
a trigger indicator is met, the operation is triggered and the
status of the operation within the limit table is updated
accordingly 276. When an activate indicator is met, the operation
is activated to create and/or add to the asset and the status of
the operation within the limit table is updated accordingly
276.
[0174] The asset modification module 30 continues to access the
limit table 268, identify operation data 270, identify evaluation
data 272, retrieve indicators 274, identify operations, and execute
the operations 282 in accordance with the identifiers to create
and/or add to the asset. Once the modification of the asset is
closed, the asset modification module 30 outputs an asset
modification result 282 (e.g., the generated or updated asset).
[0175] FIG. 15 is a schematic block diagram of another example of
an asset modification module 30 modifying an asset. In this
example, the asset modification module 30 selects one of the assets
254 to manage and retrieves the operational data 264 regarding the
asset in accordance with a selection process. The asset
modification module 30 also selects an operation set from a pool or
operation sets 294 and selects a limit table 266 from a pool of
limit tables 256 .
[0176] The asset modification module 30 accesses the selected limit
table 268 to retrieve one or more rows of information 270 (e.g., a
row of information corresponds to information regarding an
operation, which may be in a particular sequence order). The asset
modification module interprets the information to identify one or
more evaluation data 272 and retrieves the corresponding evaluation
data 280. The asset modification module 30 further interprets the
information to identify indicators 274 (e.g., trigger, detrigger,
activate, deactivate, etc.). The asset modification module 30 also
interprets the information to identify one or more operations from
the selected operation set 294.
[0177] Having retrieved indicators and the operation(s), the asset
modification module 30 analyzes the retrieved evaluation data 260
in light of the indicators. When a trigger indicator is met, the
operation is triggered and the status of the operation within the
limit table is updated accordingly 276. When an activate indicator
is met, the operation is activated for execution and the status of
the operation within the limit table is updated accordingly 276.
The asset modification module 30 executes an activated operation
282 in accordance with the indicators of the limit table to produce
a partial modification resultant.
[0178] The asset modification module 30 continues to access the
limit table 268, identify operation data 270, identify evaluation
data 272, retrieve indicators 274, identify operations, and execute
the operations 282 in accordance with the identifiers. Once the
modification of the asset is closed, the asset modification module
30 outputs an asset modification result 262.
[0179] FIG. 16 is a schematic block diagram of another example of
an asset modification module 30 modifying an asset (e.g. creating
the asset or adding to the asset 286). In this example, the asset
modification module 30 selects one of the assets 254 to create or
add to and uses this information to select an operation set from a
pool of operation sets 294 and to select a limit table 266 from a
pool of limit tables 256.
[0180] The asset modification module 30 accesses the selected limit
table 268 to retrieve one or more rows of information 270 (e.g., a
row of information corresponds to information regarding an
operation, which may be in a particular sequence order). The asset
modification module 30 interprets the information to identify one
or more evaluation data 272 and retrieves the corresponding
evaluation data 280. The asset modification module 30 further
interprets the information to identify indicators 274 (e.g.,
trigger, detrigger, activate, deactivate, etc.). The asset
modification module 30 also interprets the information to identify
one or more operations from the set of operations 294.
[0181] Having retrieved indicators and the operation(s), the asset
modification module 30 analyzes the retrieved evaluation data 260.
When a trigger indicator is met, the operation is triggered and the
status of the operation within the limit table is updated
accordingly 276. When an activate indicator is met, the operation
is activated to create and/or add to the asset and the status of
the operation within the limit table is updated accordingly
276.
[0182] The asset modification module 30 continues to access the
limit table 266, identify operation data 270, identify evaluation
data 272, retrieve indicators 274, identify operations 270, and
execute the operations 282 in accordance with the identifiers to
create and/or add to the asset. Once the modification of the asset
is closed, the asset modification module 30 outputs an asset
modification result 262 (e.g., the generated or updated asset).
[0183] FIG. 17 is a schematic block diagram of another embodiment
of an asset modification module 30 that includes a limit table
interface module 296, a plurality of indicator buffers 298-304,
memory 306-308, an operation selection module 310, a specific task
module (i.e. an operation execution module) 312, a resultant
analysis module 314, a trigger operation execution module 316, an
execute/pause (activate/deactivate) operation execution module 318,
and an evaluation data filter 320. The indicator buffers 298-304
include an operation data indicator buffer 298, an evaluation data
indicator buffer 300, a trigger indicator buffer 302, and an
execute/pause (activate/deactivate) indicator buffer 304. The
memory 306-308 stores filtered evaluation data 306, operation data
308, and may further store the resultant(s) of the operation
execution module 312. Note that each of the modules may be
implemented as a software module executable by a processing module,
may be implemented as a hard coded module, and/or may be
implemented as a firmware module (e.g., a combination of software
and hardware).
[0184] In an example of operation, the limit table interface module
296 interfaces with a selected limit table 322 to retrieve one or
more rows of information, where the per row information includes
sequence (ordering), operation identifier (ID), one or more trigger
indicators (TI), one or more detrigger indicators (DT), evaluation
data indicators (EDI), execute (or activate) indicators (EI), pause
(or deactivate) indicators (PI), operational data indicators (ODI),
current operation status, and/or execution module selection. The
one or more rows of information will correspond to the present
and/or next sequences of the asset modification process. For
example, when commencing the asset modification process, the limit
table interface module 296 will retrieve the one or more rows of
information with a sequence number of 0. As another example, when
the asset modification process is in a sequence transition state,
the limit table interface module 296 will retrieve the one or more
rows of information for the next possible sequence, or sequences.
Alternatively, the limit table interface module 296 may retrieve
row information from the limit table 322 from one row at a time to
all rows of the limit table 322.
[0185] The limit table interface module 296 provides the retrieved
indicators to the respective indicator buffers 298-304 for
temporary storage therein. For example, the limit table interface
module 296 provides the trigger indicator(s) and the detrigger
indicator(s) to the trigger indicator buffer 302; provides the
execute (activate) indicator(s) and the pause (deactivate)
indicator(s) to the execute/pause indicator buffer 304; provides
the evaluation data indicator(s) to the evaluation data buffer 300;
and provides the operation data indicator(s) to the operation data
indicator buffer 298. The limit table interface module 296 provides
the operation ID of the operation data indicator(s) to the
operation selection module 310.
[0186] The operation selection module 310 retrieves one or more
operations 324 based on the operation ID(s) and temporarily stores
them. The operation selection module 310 may retrieve the
operation(s) 324 from a pool of operations, from the limit table
that includes in the operations, and/or from an operation set
identified by the limit table. Further, the operation selection
module 310 may retrieve the operation 324 as an algorithm for
execution by the operation execution module 312. For example, the
operation 324 may be a logic function, a mathematical function, an
algorithm, and/or an operational instruction. As more specific
examples but far from an exhaustive list, the operation 324 is an
algorithm to purchase stock; is an algorithm to sell sporting event
tickets; is an algorithm to update a file on a person with newly
found evaluation data; is an algorithm to determine when to place a
purchase order for a component of a manufactured product; is an
algorithm to generate an alarm when the national weather service
has issued a severe storm warning for a region; and/or is a
function to change the formatting of a selected evaluation
data.
[0187] The evaluation data filter 320 retrieves evaluation data 326
based on the evaluation data indicator(s) and filters the data in
accordance with the evaluation data indicator(s). The evaluation
data filter 320, which functions as previously described with
reference to FIGS. 7-8A, provides filtered evaluation data to the
filtered evaluation data memory 306 for storage therein.
[0188] The trigger operation execution module 316 functions like
the wake-up sleep module of FIGS. 7 and 8 to interpret the filtered
evaluation data in light of the trigger indicator(s) to determine
whether to trigger an operation. When the operation is to be
triggered, the trigger operation execution module 316 provides a
trigger indicator to the operation selection module 310. The
operation selection module 310 records the triggering of the
operation and may load the operation into the operation execution
module 312 depending on current use of the operation execution
module 312. For instance, if this is the only operation triggered
and no other operations are activated, then the operation selection
310 may load the operation 328 into the execution module 312. If,
however, the execution module 312 is executing an operation, the
operation selection module 310 will wait to load the specific task
(i.e. operation) 328 into the execution module 312.
[0189] The execute/pause operation execution module 318 functions
like the execute activate/deactivate module of FIGS. 7 and 8 to
interpret the filtered evaluation data in light the activate
(execute) indicator(s) to determine whether to activate an
operation. When the operation is to be activated, the execute/pause
operation execution module 318 provides an activation indicator to
the operation selection module 310, which it records. The operation
selection module 310 controls program execution, interrupts, and
multitasking for the operation execution module, such that the
operation execution module 310 may execute one or more operations
efficiently. As such, when an operation is activated, the operation
selection module 310 enables the operation execution module 312 to
execute the operation. Alternatively, the asset modification module
30 may include a plurality of operation execution modules, where
the operation selection module 310 selects with operation execution
module to execute which activated operation.
[0190] The operation execution module 312 executes a selected
operation 328 on operation data 330, filtered evaluation data,
and/or a previous resultant to produce a resultant 332. The
resultant 332 corresponds to a modification of the asset. For
example, purchase stock, sell stock, sell sporting event tickets,
buy sporting event tickets, update a person's file, issue a severe
weather warning, etc.
[0191] The resultant analysis module 314 analyzes the resultant 332
to determine whether to deactivate and/or detrigger the present
operation, to place the asset modification process in a transition
state, to transition the asset modification process to a next
sequence state, etc. In addition, the resultant analysis module 314
provides status information to the limit table interface module 296
such that it can update the status field within the limit table 322
regarding the present operation.
[0192] For multitasking of several rows of information of a limit
table 322, a user device or service provider device may include
multiple asset modification modules 30, each processing one or more
assigned rows of information. Additionally, or in the alternative,
the asset modification module 30 manages multitasking of the
operations at each level of the module (e.g., the evaluation data
filter 320, operation execution module 312, the execute/pause
operation execution module 318, the trigger operation execution
module 316, etc.). As another alternative, the asset modification
module 30 includes a plurality of evaluation data filters, a
plurality of operation execution modules, a plurality of
execute/pause operation execution modules, and/or a plurality of
trigger operation execution modules.
[0193] FIG. 17A is a schematic block diagram of another embodiment
of an asset modification module 30 operably coupled to an
operational processing module (e.g., operation module) 334. The
asset modification module 30 includes a limit table interface
module 296, a plurality of indicator buffers 298-304, memory
306-308, an operation selection module 310, and a resultant
analysis module 314. The indicator buffers 298-304 include an
operation data indicator buffer 298, an evaluation data indicator
buffer 300, a trigger indicator buffer 302, and an execute/pause
(activate/deactivate) indicator buffer 304. The memory 306-308
stores filtered evaluation data 306, operation data 308, and may
further store the resultant(s) of the operation execution module
312. Note that each of the modules may be implemented as a software
module executable by a processing module, may be implemented as a
hard coded module, and/or may be implemented as a firmware module
(e.g., a combination of software and hardware). The operation
module 334 includes a specific task execution module (e.g., an
operation execution module) 312, trigger operation execution module
316 and an execute/pause (activate/deactivate) operation execution
module 318), and an evaluation data filter 320. Note that the
operation module 334 may further include a filtered data analysis
module, which may be a separate module or embedded in one or more
of the evaluation data filter 320, the execute/pause module 318,
and the trigger module 316.
[0194] In an example of operation, the asset modification module 30
identifies a selected operation module 334 and interfaces with it.
The interfacing may be locally (e.g., via a computing device bus
structure, an application program interface (API), etc.) or may be
remotely (e.g., via a WLAN interface, a LAN interface, a WAN
interface, an Internet interface, etc.). Once the asset
modification module 30 is operably coupled to the selected
operation module 334, the coupled pair functions in a similar
manner as the asset modification module 30 of FIG. 17. Note that
the asset modification module 30 may be operably coupled to
multiple selected operation modules at a given time using a
multitasking function.
[0195] FIGS. 18-20 are a logic diagram of another method for
modifying an asset in accordance that may be performed by the asset
modification module of FIG. 17 and/or 17A. The method begins with
the asset modification module selecting a limit table for a
particular asset 336. The method continues with the asset
modification module determining an initial sequence based on one or
more entries of the limit table 338. For example, the asset
modification module identifies the operations having a sequence
number of 0 and may further identify the operations having the next
one or more sequence numbers. As a specific example, the asset
modification module may identify the sequence number and ordering
from information contained in the limit table or it may determine
it based on the operations, evaluation data being analyzed, and/or
the indicators.
[0196] The method then branches into three branches. In the first
branch, the asset modification module identifies evaluation data
indicators for the operation(s) of the initial sequence (e.g.,
sequence 0) from the limit table 340. This branch continues with
the asset modification module determining whether there is
evaluation data to retrieve 342. If yes, the branch continues with
the asset modification module retrieving the identified evaluation
data 344. If not, or after the evaluation data is retrieved, this
branch continues on FIG. 19.
[0197] In the second branch, the asset modification module
identifies one or more operations associated with the initial
sequence 346. For example and with reference to FIGS. 10A-10C, the
asset modification module identifies operations A and B. This
branch continues with the asset modification module retrieving the
one or more operations 348. For example, the asset modification
module of FIG. 17 may retrieve operation instructions of an
algorithm corresponding to the operation(s) of the initial
sequence. As another example, the asset modification module of FIG.
17A establishes an operable coupling with the operation modules of
the initial sequence. This branch continues with the asset
modification module retrieving the indicators (trigger, detrigger,
activate, and deactivate) for each of the operations 350. This
branch then continues on FIG. 19.
[0198] In the third branch, the asset modification module
identifies operation data indicators for the selected operations of
the initial sequence 352. This branch continues with the asset
modification module determine whether there is at least one
operation data indictor (e.g., operation data ID, data allotment,
and/or data operands) to retrieve 354. For example, if the
operations for the initial sequence are to open an asset
modification process of the limit table, there may not be any
operation data indicators to retrieve. If there is an operation
data indicator to retrieve, this branch continues with the asset
modification module retrieving it 356. Once the asset modification
module has, or if there are no operation data indicators to
retrieve, this branch continues on FIG. 19.
[0199] On FIG. 19, the second branch continues with the asset
modification module determining whether the operation set is
complete 358. For example, the asset modification module is
determining whether the asset modification process for the selected
asset is being closed. If yes, the method continues with the asset
modification module updating 360, if needed, status fields in the
limit table indicating that the asset modification process is being
closed 362. If the asset modification process is not being closed,
the method continues with the asset modification module and/or the
selected operation module determining whether a trigger indicator
for an operation is met 364. If not, the method remains in a loop
until the asset modification process is closed or an operation is
triggered. Note that the first branch of FIG. 18 ties into the step
of determining whether a trigger indicator is met.
[0200] When a trigger indicator is met, the method continues with
the asset modification module and/or selected operation module
triggering the operation 366. The method continues with the asset
modification module and/or selected operation module determining
whether an execution (or activate) indicator is met 368. If not,
the method branches back to the step of determining whether a
trigger indicator is met for another operation 364. Also, for the
current triggered operation, the method continues with the asset
modification module and/or selected operation determining whether a
detriggering indicator is met 370. If yes, the current operation is
detriggered 372. If not, the method loops back to determining
whether an activate indicator is met 368.
[0201] When, for a triggered operation, an activate indicator is
met, the method continues with the asset modification module and/or
selected operation executing the operation to produce a result 374.
The method continues with the asset modification module (via the
results analysis module) determining whether the results indicate
whether the asset modification process should proceed to the next
sequence 376. If yes, the method continues with the asset
modification module and/or selected operation module stopping
execution of the operations of the current sequence state and
placing them in a triggered state or a detriggered state depending
on the next sequence 378. For example, if the next sequence could
be a repeat of the current sequence, then the operations are placed
in the triggered state. If, however, the next sequence could not be
a repeat of the current sequence, then the operations are placed in
a detriggered state. Regardless of the state of the operations are
placed in, the method continues on FIG. 20. If the results of the
executed operation do not indicate transitioning to the next
sequence state, the method continues with the asset modification
module and/or the selected operation module determining whether a
deactivate (or pause) indicator is met for the operation 380. If
not, the method continues in three branch back paths. The first
branch back path is for the operation being executed, which loops
back to the step of executing the operation 374. The second branch
back path is for triggered operations, which loops back to the step
of determining whether an activation indicator is met 368. The
third branch back path is for other operations of the current
sequence that are not yet triggered, which loops back to the step
of determining whether a trigger indicator is met 358.
[0202] If a deactivate indicator is met, the method continues with
the asset modification module and/or the selected operation module
stopping execution of the operation 382. The method then continues
with the asset modification module determining whether other
operations are still executing (e.g., are still activated) 384. If
not, the method continues in two branch back paths. The first
branch back path is for triggered operations, which loops back to
the step of determining whether an activation indicator is met 368.
The second branch back path is for other operations of the current
sequence that are not yet triggered, which loops back to the step
of determining whether a trigger indicator is met 358.
[0203] When another operation is still executing, the method
continues in three branch back paths. The first branch back path is
for the operation being executed, which loops back to the step of
executing the operation 374. The second branch back path is for
triggered operations, which loops back to the step of determining
whether an activation indicator is met 368. The third branch back
path is for other operations of the current sequence that are not
yet triggered, which loops back to the step of determining whether
a trigger indicator is met 358.
[0204] When the asset modification process is in a transition
state, the method continues on FIG. 20 with the asset modification
module determining whether end the operation set (e.g., close the
asset modification process) 386. If yes, the method continues with
the asset modification module updating status 388 in the limit
table and outputting a resultant, if any 390.
[0205] If the asset modification process is not being closed, the
method continues with the asset modification module transitioning
the process to the next sequence, which branches into three
branches. In the first branch, the asset modification module
identifies evaluation data indicators for the operation(s) of the
next sequence, or sequences, from the limit table 392. This branch
continues with the asset modification module determining whether
there is evaluation data to retrieve 394. If yes, the branch
continues with the asset modification module retrieving the
identified evaluation data 396. If not, or after the evaluation
data is retrieved, this branch continues on FIG. 19.
[0206] In the second branch, the asset modification module
identifies one or more operations associated with the next
sequence(s) 398. This branch continues with the asset modification
module retrieving the one or more operations 400. For example, the
asset modification module of FIG. 17 may retrieve operation
instructions of an algorithm corresponding to the operation(s) of
the initial sequence. As another example, the asset modification
module of FIG. 17A establishes an operable coupling with the
operation modules of the initial sequence. This branch continues
with the asset modification module retrieving the indicators
(trigger, detrigger, activate, and deactivate) for each of the
operations 402. This branch then continues on FIG. 19.
[0207] In the third branch, the asset modification module
identifies operation data indicators for the selected operations of
the next sequence(s) 404. This branch continues with the asset
modification module determine whether there is at least one
operation data indictor (e.g., operation data ID, data allotment,
and/or data operands) to retrieve 406. If there is an operation
data indicator to retrieve, this branch continues with the asset
modification module retrieving it 408. Once the asset modification
module has, or if there are no operation data indicators to
retrieve, this branch continues on FIG. 19.
[0208] FIG. 21 is a schematic block diagram of an example of an
asset modification module 30 selecting a limit table for asset
modification. The asset modification module 30 includes an asset
management function 410 (e.g., software and/or hardware module),
which functions to select an asset to modify and a limit table to
provide the asset modification process.
[0209] In an example of selecting an asset, the asset management
function 410 receives asset selection criteria 412, which includes,
but is not limited to, an asset ID, time of day, a time period,
evaluation data analysis for a particular result, etc. The asset
selection criteria 412 may be a user input and/or may be
automatically generated. For example, asset selection criteria 412
may include an asset ID, a start time of day, an end time of day,
and day of week. As a more specific example, the asset selection
criteria 412 may include an asset ID for a particular stock for
which an asset modification process is to be engaged from 9 AM to 4
PM eastern time on Monday through Friday. As another example, the
asset selection criteria 412 may include analysis of an inventory
list for a product, where, when an inventory level falls below a
certain threshold for a particular component, identify the
particular component as the asset for an asset modification
process. As yet another example, asset selection criteria 412 may
include availability of certain evaluation data and may further
include an evaluation data analysis criterion.
[0210] The asset management function 410 compares the asset
selection criteria 412 with factors for assets 414-416 of a pool of
assets to determine whether an asset modification process should be
engaged for a particular asset. The factors 414-416 include, but
are not limited to, a type of asset, modification timing (e.g.,
time of day, inventory depletion, etc.), evaluation data sources of
interest availability, evaluation data analysis result (e.g.,
pattern mapping, trend detection, value thresholds, comparative
analysis, etc.). As an example, asset selection criteria 412 may
include availability of a particular evaluation data source(s) and
an asset factor includes, when the particular evaluation data is
available, engage an asset modification process for the asset. As
another example, asset selection criteria 412 include an evaluation
data analysis that produces a result that correlates with an
evaluation data analysis result of the factors of an asset
414-416.
[0211] Once an asset is selected for modification, the asset
management function 410 selects a limit table based on limit table
selection criteria 418 and attributes of limit tables 420
associated with the selected asset. The limit table selection
criteria 418 include user inputs and/or auto-generated inputs
regarding, but not limited to, desired risk level, desired level of
evaluation data relevancy, asset modification philosophy, desired
reliability level, desired level of evaluation data mapping (to
trends, patterns, values, transitions, slopes, quantities, pricing,
availability, etc.), a desired level of performance, etc. The
attributes of a limit table 420 include one or more of, but not
limited to, risk level, evaluation data relevancy, asset
modification philosophy, reliability level, evaluation data mapping
accuracy, performance information, etc.
[0212] The risk level corresponds to a risk-reward relationship of
modifying the asset. A low risk level indicates a relatively low
risk that the asset will be modified in a modest favorable manner.
As an example for inventory control of a manufacturing process, the
risk-reward relationship will vary based on whether price, on-hand
availability, shipping, suppliers, etc. is/are a primary priority.
As such, the risk level corresponds to how willing a user is to
compromise adversely affecting the manufacture of a product to get
a desired reward for a particular aspect of a component, or
components.
[0213] The relevancy of evaluation data corresponds to how relevant
the evaluation data has been in the past as a source for a
predictable modification of an asset. The more predictable the
modification of asset is based on analysis of a particular
evaluation data, the more relevant the particular evaluation data.
The relevancy of evaluation data may also include a weighting
factor based on how many times the evaluation data has been
accessed for modifying an asset and/or, of the times accessed, how
many times has it been used to provide a predictable modification
of the asset.
[0214] The reliability level corresponds to how reliable the limit
table has been in producing a favorable asset modification. The
more consistently the limit table produces similar asset
modification results, the more reliable it is. Conversely, the more
varied the asset modification results, the lower the reliability is
of the limit table.
[0215] The evaluation data mapping corresponds to how close aspects
(e.g., trends, patterns, values, waveform, transitions, slopes,
quantities, pricing, availability, etc.) of evaluation data are to
be mapped to trip a trigger indicator and/or an activation
indicator. For example, if an evaluation data is being analyzed for
a specific waveform, the attributed indicates how closely the
waveform of the evaluation data needs to match the specific
waveform to trip an indicator.
[0216] The performance level corresponds to previous performances
of the limit table. The performance level may include information
regarding amount of previous asset modifications, variations of
previous asset modifications, average of previous asset
modifications, etc.
[0217] Accordingly, for a selected asset, the asset management
function 410 selects a limit table having attributes 420 that
correlate to the limit table selection criteria 418. When a near
exact match cannot be made (e.g., with acceptable tolerances of a
few percent to tens of percent), the asset modification function
410 selects a limit table having a most favorable correlation of
its attributes 420 to the limit table selection criteria 418.
Further, the asset modification function 410 may have a threshold
for determining a most favorable correlation of a limit table's
attributes to the limit table selection criteria 418. If the
threshold is not met, then the asset modification module 410 does
not select a limit table. If this occurs, the asset modification
module 410 may trigger generating a limit table that has attributes
420 that more favorably correlate to the limit table selection
criteria 418.
[0218] FIG. 22 is a diagram of an example of an asset's factors
422, which are time varying and updated on a regular basis (e.g.,
by the minute, hourly, daily, etc.) based on evaluation data. In
this example, the asset includes a plurality of factors 422 (A
through X). A factor 422 may be determined based on performance
information of various limit tables over time, where each factor
corresponds to a particular evaluation data set A-X (which includes
one or more evaluation data). For a given evaluation data set, its
affects on each limit table for a given asset is analyzed to
produce limit table performance information. As is shown in this
example, limit tables "a" through `k" for asset 1 are being
processed. Note that similar processing may be done for one or more
other limit tables of one or more other assets.
[0219] As is also shown, time 424 goes from left to right of the
illustration (e.g., older data to the right and newer data to the
left). The time is divided into frames, where the evaluation data
is analyzed during a particular time frame. The duration of a time
frame may vary from microseconds, seconds, minutes, hours, days,
weeks, and/or years. The contribution to a factor may be weighted
by giving more or less weight to more recent data. Other weighting
factors may include time of day, quantity of data, source,
deviations from past patterns, etc.
[0220] For factor A, the more recent set of evaluation data is
analyzed for each of the limit tables a-k for asset 1 to produce
performance information. The performance information includes, for
a limit table, data values, patterns, volatility, slopes,
deviations, pattern repeating probabilities, pattern transition
tendencies, trend probabilities, trend transition tendencies, etc.
and how the modification of the asset is affected. For example,
certain patterns may cause the asset to be consistently modified in
a particular way, while other patterns have no predictable
correlation to how the asset is modified. As another example,
various combinations of the performance information provides
predictable modifications of an asset at a given predictability
rate (e.g., 75% of the time). Such information is compiled and
interpreted to generate a corresponding factor, which may include a
favorable pattern, an unfavorable pattern, a desire data value, a
desired slope, acceptable data volatility, etc. Further, the
various factors of an asset may be determined based on the
performance information. For example, modification timing may be
determined based on the performance information.
[0221] The other factors are processed in a similar way to produce
limit table performance information for each of the limit tables
a-k for asset 1. The factors can be used to select with evaluation
data sets to use, which limit table to use, etc. Note that the
factors are updated by adding more recent time of data sets, by
deleting older times of data sets, changing evaluation data within
a set of evaluation data, changing a limit table, adding a limit
table, deleting a limit table, changing user performance
preferences, etc.
[0222] FIG. 23 is a diagram of an example of attributes of limit
tables, which are time 424 varying and updated on a regular basis
(e.g., by the minute, hourly, daily, etc.) based on evaluation data
426-430. In this example, the attributes for a limit table include
a risk level, evaluation data relevancy, asset modification
philosophy, reliability level, pattern mapping, etc., which, for
each limit table, is determined based on the performance
information of the limit table collected over time.
[0223] In addition, one or more experimental limit tables may be
tested over time to determine its attributes. Depending on the
quality of the attributes, the limit table may be added to a list
of usable limit tables. If the quality of the attributes is not at
a desired level, one or more entries of the limit may be changed
and further testing may be done to obtain a higher quality of the
attributes.
[0224] FIG. 24 is a logic diagram of a method of opening an asset
as may be performed by an asset management function. The method
begins with the asset management function monitoring asset
selection criteria, which may be one or more of a user input,
analysis of evaluation data, time of day, an evaluation data
source, availability of new evaluation data, etc. 432. The method
continues with the asset management function determining whether
the asset selection is auto enabled or signal enabled 434.
[0225] When the asset selection is signal enabled (e.g., a user
provides an input to select a particular asset), the method
continues with the asset management function determining whether
the select signal is detected 436. Once the signal is detected, the
method continues with the asset management function opening the
asset and placing it on an open list (e.g., a list of assets that
are currently open) 438. Alternatively, a status flag associated
with the asset may be set to an open status. The method then
proceeds to the selecting a limit table for the asset 440, which is
discussed in FIG. 25.
[0226] When the asset selection is auto enabled, the method
continues with the asset management function analyzing factors of
each evaluation data set with respect to current evaluation data
442. Recall that factors include one or more of asset type,
modification timing, evaluation data sources of interest,
evaluation data pattern mapping, which includes favorable patterns,
unfavorable patterns, desired data values, desired data slopes,
acceptable data volatility, favorable comparison between evaluation
data, and/or other favorable analysis of one or more evaluation
data. The current evaluation data may include one or more of the
data sets associated with an asset portfolio. As an example, the
method may be configured to determine if a particular asset should
be auto opened. In this instance, the evaluation data set(s) would
be limited to the sets associated with the particular asset. As
another example, the method may be configured to determine if any
one or more of the assets should be opened. In this instance, the
evaluation data sets would not be limited to a particular
asset.
[0227] The analysis of the factors may include a mathematical
operation, a logic operation, a filtering operation, a statistical
operation, a combination thereof, multiple combinations thereof,
and/or multiple iterations of one or more of the operations. For
instance, the analysis may include an averaging function, a
standard deviation function, a normalizing function, a root mean
square function, weighting function, digital logic function (e.g.,
OR, AND, NOR, XOR, etc.), etc.
[0228] The method continues with the asset management function
selecting an evaluation data set based on the analysis 444. For
example, current evaluation data set A is tracking within desired
tolerances of the factors for a given asset. If more than one
evaluation data set is tracking within desired predictable aspects,
one or more of them might be selected. For example, the asset
management function may select the one that is more closely maps to
desired patterns, trends, etc. As another example, the asset
management function may select several evaluation data sets.
[0229] The method continues with the asset management function
monitoring the current evaluation data of the selected evaluation
data set(s) in light of the factors of an associated asset 446. For
example, the current evaluation data is monitored for patterns,
trends, etc. indicating that, in the near future (in the next few
milliseconds, the next seconds, the next minutes, the next hours,
the next days, the next weeks, the next months, etc.), that
favorable asset modification is probable and may further indicate
an indication as to a level of probability. For example, when, in
the past, the evaluation data exhibited certain patterns, trends,
etc. favorable asset modification occurred at a certain ratio
(e.g., 10 out of 10, 6 out of 11, etc.).
[0230] The method continues with the asset management function
determining whether the factors compare favorably to the relevant
asset selection criteria (e.g., a set of criterion expressing a
desired level of probability for each factor of interest, which may
be weighted) 448. For instance, trends or patterns may be given
more weight than the evaluation data being at a specific data
value. When the comparison is favorable, the method continues with
the asset management function opening the asset and placing it on
an open list (or setting a status flag) 438. The method then
proceeds to the selecting a limit table for the asset 440, which is
discussed in FIG. 25.
[0231] If the comparison was not favorable, the method continues by
determining whether there is another asset not open 450. If not,
the method waits until one or more assets are not open. If there is
at least one asset not open, the method goes to the next asset 452
and repeats the method at the analysis factors step 442.
[0232] FIG. 25 is a logic diagram of a method of selecting a limit
table for an asset as may be performed by an asset management
function. The method begins with the asset management function
determining whether the limit table selection is auto enabled or
signal enabled 454. When the limit selection is signal enabled
(e.g., a user provides an input to select a particular asset), the
method continues with the asset management function determining
whether the select signal is detected 456. Once the signal is
detected, the method continues with the asset management function
opening the limit table 458. The method continues with the asset
management function determining whether to open another limit table
for the asset 460. If not, the method is complete for this asset
modification process.
[0233] If the limit table selection is automated, the method
continues with the asset management function analyzing attributes
of one or more limit tables with respect to an evaluation data set
(e.g., the selected evaluation data set) 462. Recall that the
attributes include, but are not limited to, one or more of risk
level, evaluation data relevancy, asset modification philosophy,
reliability level (e.g., proven, unproven, works some times, etc.),
favorable evaluation data patterns and/or trends, performance
information, etc. The limit table to initially analyze may be
randomly chosen, may be based on history of use for the asset,
based on indicators, based on the selected evaluation data set,
etc.
[0234] The method continues with the asset management function
analyzing attributes of the one or more limit tables with respect
to user performance preferences, which include, but are not limited
to, a desired risk level, a specific asset modification philosophy,
a desired reliability level, a desired level of evaluation data
mapping, a desired performance level, etc. 464. Such a
determination may be done by a table look up, a query-response
process, receiving user inputs, etc.
[0235] The method continues with the asset management function
determining whether the analysis was favorable 466. If yes, the
method continues with the asset management function opening the
limit table 458. The method continues with the asset management
function determining whether to open another limit table for the
asset 460. If not, the method is complete for this asset
modification process.
[0236] If the attributes did not compare favorably, the method
continues with the asset management function determining whether
all limit tables have been tested 468. If not, another limit table
is selected and the process repeats as shown 470. If all of the
limit tables have been tested, the method continues with the asset
management function determining whether to select another
evaluation data set and/or to change the user preferences 472. If
not, the process is closed and a limit table is not opened 474. If
yes, the method repeats as shown.
[0237] FIG. 26 is a schematic block diagram of another example of
an asset modification module 30 modifying an asset. In this
example, the asset modification module 30 selects one of the assets
254 to manage and retrieves the operational data regarding the
asset in accordance with a selection process 264. Once the asset
modification module 30 selects an asset for modification, it
selects an operation set limit table 266 from a plurality of limit
tables 256. In this example, asset 1 has limit tables 1_1 through
1_.alpha. available and the asset modification module 30 selects
one of them based on attributes and/or factors of the limit tables
(e.g., risk levels, reliability, etc.) in accordance with user
preferences or a calculated preference.
[0238] The asset modification module 30 accesses the selected limit
table 268 to retrieve one or more rows of information 270 (e.g., a
row of information corresponds to information regarding an
operation, which may be in a particular sequence order). The asset
modification module 30 interprets the information to identify one
or more evaluation data 272 and retrieves the corresponding
evaluation data 280. The asset modification module 30 further
interprets the information to identify indicators 274 (e.g.,
trigger, detrigger, activate, deactivate, etc.). The asset
modification module 30 also interprets the information to identify
one or more co-processors from a pool of co-processors 476 to
perform a particular operation (e.g., operation l, k, x,
.psi.).
[0239] Having retrieved indicators and identified the
co-processor(s), the asset modification module 30 provides the
retrieved evaluation data 260 and the indicators to the identified
co-processor(s) for analysis 478. When a trigger indicator is met,
the co-processor triggers an operation and informs the asset
modification module 30 thereof. The asset modification module 30
updates the status of the operation within the limit table 276.
When an activate indicator is met, the co-processor activates the
operation for execution and informs the asset modification module
30 thereof. The asset modification module updates the status of the
operation within the limit table 276. The co-processor executes an
activated operation in accordance with the indicators of the limit
table to produce a partial modification resultant 480, which it
provides to the asset modification module 30. Alternatively, the
asset modification module 30 analyzes the evaluation data 482 to
determine the triggering and activation of an operation. In this
alternative, the asset modification module 30 provides a trigger
signal and an activation signal to co-processor regarding the
operation.
[0240] The asset modification module 30 continues to access the
limit table 266, identify operation data 270, identify evaluation
data 272, retrieve indicators 274, identify co-processors 284, and
receives resultants from the co-processors 480 in accordance with
the identifiers. Once the modification of the asset is closed, the
asset modification module outputs an asset modification result
262.
[0241] FIG. 27 is a schematic block diagram of another example of
an asset modification module 30 modifying an asset (e.g. creating
the asset or adding to the asset 484). In this example, the asset
modification module 30 selects one of the assets 254 to create, or
add to, and uses this information to select an operation set limit
table 266 from a plurality of limit tables 256.
[0242] The asset modification module 30 accesses the selected limit
table 268 to retrieve one or more rows of information (e.g., a row
of information corresponds to information regarding an operation,
which may be in a particular sequence order). The asset
modification module 30 interprets the information to identify one
or more evaluation data and retrieves the corresponding evaluation
data 272. The asset modification module 30 further interprets the
information to identify indicators 274 (e.g., trigger, detrigger,
activate, deactivate, etc.). The asset modification module 30 also
interprets the information to identify one or more co-processors
284 from a pool of co-processors 476.
[0243] Having retrieved indicators and identified the
co-processor(s), the asset modification module 30 forwards the
indicators, the evaluation data, and operation data to the
co-processor(s) 478 for analysis. When a trigger indicator is met,
the co-processor triggers the operation and informs the asset
modification module 30 thereof. The asset modification module 30
updates the status of the operation within the limit table 276.
When an activate indicator is met, the co-processor activates the
operation to create and/or add to the asset and informs the asset
modification module 30 thereof. The asset modification module 30
updates the status of the operation within the limit table 276.
Upon execution of the operation, the co-processor provides
resultants 480 to the asset modification module 30. Alternatively,
the asset modification module 30 analyzes the evaluation data 482
to determine the triggering and activation of an operation. In this
alternative, the asset modification module 30 provides a trigger
signal and an activation signal to co-processor regarding the
operation.
[0244] The asset modification module 30 continues to access the
limit table 266, identify operation data 268, identify evaluation
data 272, retrieve indicators 274, identify co-processors 284, and
receive resultants from the co-processors 480 in accordance with
the identifiers to create and/or add to the asset. Once the
modification of the asset is closed, the asset modification module
30 outputs an asset modification result 262 (e.g., the generated or
updated asset).
[0245] FIG. 28 is a schematic block diagram of another example of
an asset modification module 30 modifying an asset. In this
example, the asset modification module 30 selects one of the assets
to manage and retrieves the operational data regarding the asset
264 in accordance with a selection process. The asset modification
module 30 also selects an operation set, which has associated
therewith an operation set of co-processors 294. For instance,
limit table 1_1 through 1_.alpha. have associated therewith,
operation set 1 of co-processors.
[0246] The asset modification module 30 accesses the selected limit
table 268 to retrieve one or more rows of information (e.g., a row
of information corresponds to information regarding an operation,
which may be in a particular sequence order). The asset
modification module 30 interprets the information to identify one
or more evaluation data and retrieves the corresponding evaluation
data 272. The asset modification module 30 further interprets the
information to identify indicators 274 (e.g., trigger, detrigger,
activate, deactivate, etc.). The asset modification module 30 also
interprets the information to identify one or more co-processors
284 from the operation set of co-processors 294.
[0247] Having retrieved the indicators and identified the
co-processor(s), the asset modification module 30 provides the
retrieved evaluation data and the indicators to the identified
co-processor(s) for analysis 478. When a trigger indicator is met,
the co-processor triggers an operation and informs the asset
modification module 30 thereof. The asset modification module 30
updates the status of the operation within the limit table 276.
When an activate indicator is met, the co-processor activates the
operation for execution and informs the asset modification module
30 thereof. The asset modification module updates the status of the
operation within the limit table 276. The co-processor executes an
activated operation in accordance with the indicators of the limit
table to produce a partial modification resultant, which it
provides to the asset modification module. Alternatively, the asset
modification module 30 analyzes the evaluation data 482 to
determine the triggering and activation of an operation. In this
alternative, the asset modification module 30 provides a trigger
signal and an activation signal to co-processor regarding the
operation.
[0248] The asset modification module 30 continues to access the
limit table 266, identify operation data 268, identify evaluation
data 272, retrieve indicators 274, identify co-processors 284, and
receives resultants from the co-processors 480 in accordance with
the identifiers. Once the modification of the asset is closed, the
asset modification module outputs an asset modification result
262.
[0249] FIG. 29 is a schematic block diagram of another example of
an asset modification module 30 modifying an asset (e.g. creating
the asset or adding to the asset 484). In this example, the asset
modification module 30 selects one of the assets 254 to create, or
add to, and uses this information to select an operation set limit
table 266 from a plurality of limit tables 256, wherein each limit
table has associated therewith an operation set of co-processors
294. For instance, limit table 1_1 through 1_.alpha. have
associated therewith, operation set 1 of co-processors.
[0250] The asset modification module 30 accesses the selected limit
table 268 to retrieve one or more rows of information (e.g., a row
of information corresponds to information regarding an operation,
which may be in a particular sequence order). The asset
modification module 30 interprets the information to identify one
or more evaluation data and retrieves the corresponding evaluation
data 272. The asset modification module 30 further interprets the
information to identify indicators 274 (e.g., trigger, detrigger,
activate, deactivate, etc.). The asset modification module 30 also
interprets the information to identify one or more co-processors
284 from a pool of co-processors 294.
[0251] Having retrieved indicators and identified the
co-processor(s), the asset modification module 30 forwards the
indicators, the evaluation data, and operation data to the
co-processor(s) for analysis 478. When a trigger indicator is met,
the co-processor triggers the operation and informs the asset
modification module 30 thereof. The asset modification module 30
updates the status of the operation within the limit table 276.
When an activate indicator is met, the co-processor activates the
operation to create and/or add to the asset and informs the asset
modification module 30 thereof. The asset modification module 30
updates the status of the operation within the limit table 276.
Upon execution of the operation, the co-processor provides
resultants 480 to the asset modification module. Alternatively, the
asset modification module 30 analyzes the evaluation data 482 to
determine the triggering and activation of an operation. In this
alternative, the asset modification module 30 provides a trigger
signal and an activation signal to co-processor regarding the
operation.
[0252] The asset modification module 30 continues to access the
limit table 266, identify operation data 270, identify evaluation
data 272, retrieve indicators 274, identify co-processors 284, and
receive resultants from the co-processors 480 in accordance with
the identifiers to create and/or add to the asset. Once the
modification of the asset is closed, the asset modification module
30 outputs an asset modification result 262 (e.g., the generated or
updated asset).
[0253] FIG. 30 is a schematic block diagram of another embodiment
of an asset modification module 30 operably coupled to a
co-processor 486. The asset modification module 30 includes a limit
table interface module 296, a plurality of indicator buffers
298-304, memory 306-308, an operation selection module 310, a
resultant analysis module 314, a trigger operation execution module
316, an execute/pause (activate/deactivate) operation execution
module 318, and an evaluation data filter 320. The indicator
buffers 298-304 include an operation data indicator buffer 298, an
evaluation data indicator buffer 300, a trigger indicator buffer
302, and an execute/pause (activate/deactivate) indicator buffer
304. The memory 306-308 stores filtered evaluation data 306,
operation data 308, and may further store the resultant(s) of the
operation execution module 312. Note that each of the modules may
be implemented as a software module executable by a processing
module, may be implemented as a hard coded module, and/or may be
implemented as a firmware module (e.g., a combination of software
and hardware). The co-processor 486 includes an operation execution
module 312.
[0254] In an example of operation, the asset modification module 30
identifies a selected co-processor 486 and interfaces with it. The
interfacing may be locally (e.g., via a computing device bus
structure, an application program interface (API), etc.) or may be
remotely (e.g., via a WLAN interface, a LAN interface, a WAN
interface, an Internet interface, etc.). Once the asset
modification module 30 is operably coupled to the selected
co-processor 486, the coupled pair functions in a similar manner as
the asset modification module 30 of FIG. 17. Note that the asset
modification module 30 may be operably coupled to multiple selected
co-processors 486 at a given time using a multitasking
function.
[0255] FIGS. 31-33 are a logic diagram of another method for
modifying an asset that may be performed by the asset modification
module of FIG. 30. The method begins with the asset modification
module selecting a limit table for a particular asset 488. The
method continues with the asset modification module determining an
initial sequence based on one or more entries of the limit table
490. For example, the asset modification module identifies the
operations having a sequence number of 0 and may further identify
the operations having the next one or more sequence numbers. As a
specific example, the asset modification module may identify the
sequence number and ordering from information contained in the
limit table or it may determine it based on the operations,
evaluation data being analyzed, and/or the indicators.
[0256] The method then branches into three branches. In the first
branch, the asset modification module identifies evaluation data
indicators for the operation(s) of the initial sequence (e.g.,
sequence 0) from the limit table 492. This branch continues with
the asset modification module determining whether there is
evaluation data to retrieve 494. If yes, the branch continues with
the asset modification module retrieving the identified evaluation
data 496. If not, or after the evaluation data is retrieved, this
branch continues on FIG. 32.
[0257] In the second branch, the asset modification module
identifies one or more co-processors associated with the initial
sequence 498. This branch continues with the asset modification
module retrieving the one or more operations. This branch continues
with the asset modification module retrieving the indicators
(trigger, detrigger, activate, and deactivate) for each of the
operations 500. This branch then continues on FIG. 32.
[0258] In the third branch, the asset modification module
identifies operation data indicators for the selected operations of
the initial sequence 502. This branch continues with the asset
modification module determine whether there is at least one
operation data indictor (e.g., operation data ID, data allotment,
and/or data operands) to retrieve 504. For example, if the
operations for the initial sequence are to open an asset
modification process of the limit table, there may not be any
operation data indicators to retrieve. If there is an operation
data indicator to retrieve, this branch continues with the asset
modification module retrieving it 506. Once the asset modification
module has, or if there are no operation data indicators to
retrieve, this branch continues on FIG. 32.
[0259] On FIG. 32, the second branch continues with the asset
modification module determining whether the operation set is
complete 508. For example, the asset modification module is
determining whether the asset modification process for the selected
asset is being closed. If yes, the method continues with the asset
modification module updating 510, if needed, status fields in the
limit table indicating that the asset modification process is being
closed 512. If the asset modification process is not being closed,
the method continues with the asset modification module determining
whether a trigger indicator for an operation is met 514. If not,
the method remains in a loop until the asset modification process
is closed or an operation is triggered. Note that the first branch
of FIG. 31 ties into the step of determining whether a trigger
indicator is met.
[0260] When a trigger indicator is met, the method continues with
the asset modification module triggering the co-processor 516. The
method continues with the asset modification module determining
whether an execution (or activate) indicator is met 518. If not,
the method branches back to the step of determining whether a
trigger indicator is met for another operation 514. Also, for the
current triggered co-processor, the method continues with the asset
modification module determining whether a detriggering indicator is
met 520. If yes, the current co-processor is detriggered 522. If
not, the method loops back to determining whether an activate
indicator is met 518.
[0261] When, for a triggered co-processor, an activate indicator is
met, the method continues by enabling the co-processor to execute
the operation to produce a result 524. Note that the first and
third branch of FIG. 31 tie into the step of enabling the
co-processor 524. The method continues with the asset modification
module (via the results analysis module) determining whether the
results indicate whether the asset modification process should
proceed to the next sequence 526. If yes, the method continues with
the asset modification module deactivating the co-processor(s) of
the current sequence state and placing them in a triggered state or
a detriggered state depending on the next sequence 528. For
example, if the next sequence could be a repeat of the current
sequence, then the operations are placed in the triggered state.
If, however, the next sequence could not be a repeat of the current
sequence, then the operations are placed in a detriggered state.
Regardless of the state of the operations are placed in, the method
continues on FIG. 33.
[0262] If the results of the executed operation do not indicate
transitioning to the next sequence state, the method continues with
the asset modification module determining whether a deactivate (or
pause) indicator is met for the co-processor 530. If not, the
method continues in three branch back paths. The first branch back
path is for the activated co-processor, which loops back to the
step of enabling the co-processor to execute the operation 524. The
second branch back path is for triggered co-processors, which loops
back to the step of determining whether an activation indicator is
met 518. The third branch back path is for other co-processors of
the current sequence that are not yet triggered, which loops back
to the step of determining whether a trigger indicator is met
508.
[0263] If a deactivate indicator is met, the method continues with
the asset modification module deactivates the co-processor from
executing the operation 532. The method then continues with the
asset modification module determining whether other co-processors
are still enabled (e.g., are still activated) 534. If not, the
method continues in two branch back paths. The first branch back
path is for triggered co-processors, which loops back to the step
of determining whether an activation indicator is met 518. The
second branch back path is for other co-processors of the current
sequence that are not yet triggered, which loops back to the step
of determining whether a trigger indicator is met 508.
[0264] When another co-processor is still activated, the method
continues in three branch back paths. The first branch back path is
for the activated co-processor, which loops back to the step of
executing the operation 524. The second branch back path is for
triggered co-processors, which loops back to the step of
determining whether an activation indicator is met 518. The third
branch back path is for other co-processors of the current sequence
that are not yet triggered, which loops back to the step of
determining whether a trigger indicator is met 508.
[0265] When the asset modification process is in a transition
state, the method continues on FIG. 33 with the asset modification
module determining whether end the operation set (e.g., close the
asset modification process) 536. If yes, the method continues with
the asset modification module updating status in the limit table
and outputting a resultant, if any 38.
[0266] If the asset modification process is not being closed, the
method continues with the asset modification module transitioning
the process to the next sequence, which branches into three
branches. In the first branch, the asset modification module
identifies evaluation data indicators for the operation(s) of the
next sequence, or sequences, from the limit table 540. This branch
continues with the asset modification module determining whether
there is evaluation data to retrieve 542. If yes, the branch
continues with the asset modification module retrieving the
identified evaluation data 544. If not, or after the evaluation
data is retrieved, this branch continues on FIG. 32.
[0267] In the second branch, the asset modification module
identifies one or more co-processors associated with the next
sequence(s) 546. This branch continues with the asset modification
module retrieving the indicators (trigger, detrigger, activate, and
deactivate) for each of the operations 548. This branch then
continues on FIG. 32.
[0268] In the third branch, the asset modification module
identifies operation data indicators for the selected operations of
the next sequence(s) 550. This branch continues with the asset
modification module determine whether there is at least one
operation data indictor (e.g., operation data ID, data allotment,
and/or data operands) to retrieve 552. If there is an operation
data indicator to retrieve, this branch continues with the asset
modification module retrieving it 554. Once the asset modification
module has, or if there are no operation data indicators to
retrieve, this branch continues on FIG. 32.
[0269] FIG. 34 is a diagram of another example of an operation set
limit table 212 that includes a plurality of columns and a
plurality of rows. The columns correspond to particular aspects of
the operation set limit table 212 such as sequence (ordering) 214,
operation identifier (ID) 216, one or more trigger indicators 218,
one or more detrigger indicators 220, evaluation data indicators
222, execute (or activate) indicators 224, pause (or deactivate)
indicators 226, operational data indicators 228, current operation
status 230, and execution module selection 232. Note that an
operation limit table 212 may include more or less columns (i.e.,
aspects). For example, the execution module selection 232 may be
omitted. As another example, the sequence column 214 may be
omitted. As yet another example, a column or aspect may be added to
indicate configuration information for the evaluation data
filter.
[0270] Each row of the operation set limit table 212 corresponds to
an identified operation and its operating conditions. For example,
a first row below the header includes an entry in the sequence
field 214 of 0, an entry in the operation ID 216 field of "A"
(e.g., open asset modification process of an asset), an entry in
the trigger indicator field 218 of "turn op on signal", an entry in
the de-trigger indicator field 220 of "turn op off signal", an
entry in the evaluation data ID field 234 of "not applicable
(n/a)", an entry in the evaluation data factors 236 of "n/a", an
entry in the execute indicator field 224 of "upon activation"
(e.g., when the turn on signal is detected), an entry in the pause
indicator 226 of "upon execution" (e.g., just after it is
executed), entries in each of the operational data indicators 228
of "n/a", an entry in the current operation status 230 of
"executed", and an entry in the execution module selection 232 of
"A-1" (e.g., use execution module A-1 to perform the
operation).
[0271] When operation A has been executed, the next sequenced
operations are triggerable, which are operations B and C since they
have a sequence number of 1. The entries for operation B include an
entry in the trigger indicator field 220 of "dm>=f1(m)", an
entry in the de-trigger indicator field 222 of
"dm<=f1(m)-f2(m)", an entry in the evaluation data ID field 234
of "dm", an entry in the evaluation data factors 2236 of "dm source
time frame", an entry in the execute indicator field 224 of
"dm>=f1(m)+3", an entry in the pause indicator 226 of
"dm<f2(m)", an entry in the operation data ID 238 of "Dc", an
entry in the data allotment 240 of "10%", an entry in the data
operands 242 of "x value", an entry in the current operation status
230 of "hold", and an entry in the execution module 232 selection
of "B-1".
[0272] For this row (i.e., operation B), the operation is to be
triggered when dm (i.e., the evaluation data set of one or more
evaluation data) is equal to or greater than a first function of
the evaluation data. The first function may be a mathematical
function, a filtering function, a logic function, a mapping
function, a statistical function, etc. For example, the first
function may be to detect when a value of the evaluation data
exceeds a threshold, when a slope of the evaluation data is at or
above a certain slope, when a pattern of the evaluation data is
within a given tolerance of a desired pattern. etc.
[0273] The operation is to be detriggered when the evaluation data
(dm) is less than the first function of the evaluation data (f1(m))
minus a second function (f2(m)). The second function may also be a
mathematical function, a filtering function, a logic function, a
mapping function, a statistical function, etc.
[0274] The evaluation data indicators 228 include the evaluation
data ID 234 of dm and an evaluation data factor(s) 236 of a given
time frame. For example, dm identifies a set of evaluation data
that includes one or more evaluation data and the dm source time
frame indicates what time of the day the data is to be
retrieved.
[0275] The execute indicator 224 indicates that the operation is to
be activated for execution when the evaluation data (dm) is equal
to or greater than a first function of the evaluation data plus 3.
The operation is to be paused (i.e., deactivated) when the
evaluation data (dm) is less than the second function on the
evaluation data.
[0276] The operation data ID 238 identifies operation data (Dc),
which corresponds to the asset being modified. The data allotment
field 240 indicates that 10% of the asset is to be subjected to
modification by operation B. The data operand field 242 indicates
an operand of the value x for operation B to use when executed. The
operand may be an initializing value, a constant for an equation, a
unit of measure (e.g., quantity, pounds, dollars, etc.), a
condition for executing the operation, an execution repetition
indication (e.g., how many times the operation is to be executed),
etc.
[0277] The current operation status 230 of "hold" indicates that
this operation will not be triggered even if the triggering
conditions occur. An operation may be put on hold when another
operation of the same sequence number is triggered or activated.
Other operation status includes triggered, activated, executed,
waiting (to be triggered), etc.
[0278] The third row of the limit table is for operation C, which
also has a sequence number of 1. Operation C is to be triggered
when dn (i.e., another evaluation data set of one or more
evaluation data) is equal to or greater than k, which may be a
constant, a slope, a pattern, a trend, etc. Operation C is to be
detriggered when the evaluation data (dn) is less thank-2.
[0279] The evaluation data indicators 222 include the evaluation
data ID 234 of dn and an evaluation data factor(s) 236 of a given
time frame. For example, dn identifies a set of evaluation data
that includes one or more evaluation data and the dn source time
frame indicates what time of the day the data is to be
retrieved.
[0280] The execute indicator 224 indicates that the operation is to
be activated for execution when the evaluation data (dn) is equal
to or greater than k+2 . Operation C is to be paused 226 (i.e.,
deactivated) when the evaluation data (dn) is less than k.
[0281] The operation data ID 238 identifies operation data (Dc),
which corresponds to the asset being modified. The data allotment
field 240 indicates that 10% of the asset is to be subjected to
modification by operation C. The data operand field 242 indicates
an operand of the value x for operation C to use when executed. The
current operation status of "triggered" indicates that this
operation is triggered, but not yet activated.
[0282] From this limited example, a variety of evaluation data can
be analyzed for a variety of characteristics to trigger,
de-trigger, activate, and de-activate an operation. For example,
the characteristics include, but are not limited to, author,
subject matter, values, trends, patterns, slopes, etc.
[0283] FIG. 35 is a diagram of another example of a generalized
operation set limit table that includes a sequence column 556, an
operation ID column 558, a general description of the operation
column 560, and a generalized indicators column 562. The first row
is for sequence 0 and identifies operation A, which is an operation
to turn on the asset modification process when a turn on signal is
detected.
[0284] The next row of the table is sequence 1 that identifies
operations B and C. Each operation, while performing different
operations, generally functions to open a position (e.g., for a
given asset, subject it to modification). Either operation B or C
is triggered and then executed upon favorable analysis of relevant
evaluation data.
[0285] The next row of the table is for sequence 2, which
identifies operation D. Operation D is a function to extend the
position of the asset in a first manner based on extend asset
development type 1 indicators. For example, operation D corresponds
to buying a certain quantity of component for a manufactured
product based on certain conditions in the relevant evaluation
data.
[0286] The next row of the table is also for sequence 2, which
identifies operations E and F. Each of operations E and F, while
performing different operations, generally functions to extend the
position of the asset in a second manner based on extend asset
development type 2 indicators. For example, each of operation E and
F corresponds to buying different quantities of component for a
manufactured product based on certain conditions in the relevant
evaluation data. As a more specific example, operation D may be to
buy x quantity when inventory is running low regardless of price
from any vendor; operation E may be to buy z quantity of the
component when the price is below a certain threshold from a
specific vendor; and operation F may be to buy y quantity of the
component when the price is below a certain threshold for a set of
vendors.
[0287] The next row of the table is for sequence 3, which
identifies operations G and H. Each of operations G and H, while
performing different operations, generally function to reduce the
position of the asset in a first manner based on reduced asset
development type 1 indicators. For example, each of operations G
and H corresponds to consuming a component in the manufacture of a
product. As a more specific example, operation G may be to use k
quantity of the component when the evaluation data indicates a
certain level of a market condition for the product; and operation
H may be use m quantity of the component when the evaluation data
indicates a different level of the market condition for the
product.
[0288] The next row of the table is also for sequence 3, which
identifies operations K and L. Each of operations K and L, while
performing different operations, generally function to reduce the
position of the asset in a second manner based on reduced asset
development type 2 indicators. For example, each of operations K
and L corresponds to selling an abundance of a component used in
the manufacture of a product. As a more specific example, operation
K may be to sell a quantity of the component when the evaluation
data indicates a particular market condition for the product; and
operation L may be sell a different quantity of the component when
the evaluation data indicates a different market condition for the
product.
[0289] The next row of the table is for sequence 4, which
identifies operations M, N, and O. Each of operations M, N, and O,
while performing different operations, generally function to close
the position of the asset when the evaluation data is unfavorable.
For example, operation M closes the asset modification process when
the evaluation data is unfavorable in a first manner; operation N
closes the asset modification process when the evaluation data is
unfavorable in a second manner; and operation O closes the asset
modification process when the evaluation data is unfavorable in a
third manner.
[0290] The last row of the table is for sequence 5, which
identifies operation P. Operation P functions to turn off the asset
modification process when a turn off signal is detected.
[0291] FIG. 36 is a diagram of an example of operation sequencing
of the limit table of FIG. 35. In this diagram, the processing
starts in sequence 0 with operation A engaged to detect the turn on
signal. Until the turn on signal is detected, the asset
modification process remain is sequence 0 (e.g., the asset
modification process is off). Once the turn on signal is detected,
the sequencing transitions to sequence 1, which is to open a
position for an asset. In sequence 1, operations B and C are
engaged to open a position as discussed above.
[0292] Once a position for an asset is opened, the asset
modification process is in a transition state and can transition
from sequence 1 to sequence 2, 3, or 4 based on the indicators. For
example, if the extend asset development per type 1 indicators are
detected in the evaluation data, the process transitions to
sequence 1 and operation D is engaged. As another example, if the
extend asset development per type 2 indicators are detected in the
evaluation data, the process transitions to sequence 1 and
operations E and/or F are engaged. As yet another example, if the
reduced asset development per type 1 indicators are detected in the
evaluation data, the process transitions to sequence 3 and
operations G and H are engaged. As yet another example, if the
reduced asset development per type 2 indicators are detected in the
evaluation data, the process transitions to sequence 3 and
operations K and L are engaged. As a further example, if the
analysis of the evaluation data is unfavorable (e.g., for a given
time period), the process transitions to sequence 4 and operations
M, N, and O are engaged to close the position for the asset.
[0293] When the process is in sequence 1, it may transition to
sequence 2, 3, or 4. When the process is in sequence 2, it may
transition to sequence 3, 4 or it may transition back to sequence
2. When the process is in sequence 3, it may transition to sequence
2, 4, or it may transition back to sequence 3. When the process is
in sequence 4, it may transition to sequence 1 or to 5. The various
transitions are based on analysis of the evaluation data in light
of the various indicators.
[0294] FIG. 37 is a schematic block diagram of another example of
an asset modification module 30 modifying an asset in accordance
with the limit table of FIG. 35 and sequence diagram of FIG. 36. In
this example, the asset modification module 30 has selected one of
the assets (e.g., asset 1) to manage and retrieves the operational
data regarding the asset in accordance with a selection process
(e.g., user selection, automated determination process, default
selection, etc.).
[0295] The asset modification module 30 then selects an operation
set limit table from a plurality of limit tables 256 (e.g., step 1
for sequence 0). In this example, asset 1 has limit tables 1_1
through 1_.alpha. available and the asset modification module 30
selects one of them based on attributes and/or factors of the limit
tables in accordance with user preferences and/or a calculated
preference. For this example, the limit table of FIG. 35 is
selected.
[0296] The asset modification module accesses the selected limit
table to retrieve the first row of information (e.g., the sequence
0 row). The asset modification module 30 interprets the information
to identify indicators (e.g., trigger, detrigger, activate,
deactivate, etc.). The asset modification module 30 also interprets
the information to identify, and retrieve, operation A from a pool
of operations 258 (e.g. steps 2 & 3 for sequence 0).
[0297] Having retrieved indicators and operation A, the asset
modification module 30 waits for the trigger and the activate
indicators to be met (e.g., detect activation of a turn on signal).
When an activate indicator is met, the asset modification module 30
executes operation A (e.g., step 4 of sequence 0). The asset
modification module 30 then updates the status of the limit table
(e.g., indicating that operation A has been executed) and places
the asset modification process in a sequence transition state
(e.g., step 5 of sequence 0).
[0298] FIG. 38 is a schematic block diagram of another example of
an asset modification module 30 modifying an asset transitioning to
sequence 1 in accordance with the limit table of FIG. 35 and
sequence diagram of FIG. 36. The processing in sequence 1 begins
with the asset modification module 30 accessing the limit table
(e.g., step 1 of sequence 1). From the limit table, the asset
modification module 30 identifies operations to retrieve (e.g.,
step 2 of sequence 1); identify operation data (e.g., step 3 of
sequence 1); identify evaluation data (e.g., step 3 of sequence 1);
and the identify indicators for the identified operations (e.g.,
step 3 of sequence 1).
[0299] In this example, the asset modification module 30 identified
operations B, C, and P from the limit table (step 2). With
reference to FIG. 36, the sequencing can transition from sequence 0
to sequence 1 or to sequence 5 after successful execution of
operation A of sequence 0. As such, operations B and C are
operations of sequence 1 and operation P is the operation of
sequence 5. In other words, once operation A is executed to turn on
the asset modification process, the process waits for either
operation B or C to be executed to open the asset for modification
or for operation P to be executed to turn off the asset
modification process.
[0300] For step 3 of sequence 1, the asset modification module 30
identifies the operation data indicators for the asset (e.g., the
asset ID, the data allotment amount, and data operands, if any). In
addition, the asset modification module 30 identifies evaluation
data for analysis and identifies the other indicators (e.g.,
trigger indicator, de-trigger indicator, execute indicator, and
pause indicator).
[0301] The processing within sequence 1 continues at step 4 where
the asset modification module 30 retrieves the identified
evaluation data and the identified operation data (if any). The
asset modification module analyzes the retrieved evaluation data in
light of the indicators. When a trigger indicator is met for one of
the operations, the operation is triggered and the status of the
operation within the limit table is updated accordingly. When an
activate indicator is met for a triggered operation, the operation
is activated for execution and the status of the operation within
the limit table is updated accordingly. The asset modification
module 30 executes the activated operation in accordance with the
indicators of the limit table to produce a partial modification
resultant (e.g., step 5 of sequence 1).
[0302] Depending on which operation is triggered, activated, and
executed, the asset modification process transitions to a next
sequence. For instance, if operation P is executed, the asset
modification process is turned off. If, however, operation B or C
is executed, the asset modification process is in a transition
state from which it could transition to sequence 2, 3, or 4.
[0303] FIG. 39 is a schematic block diagram of another example of
an asset modification module 30 modifying an asset transitioning
from sequence 1 to sequence 2, 3, or 4 in accordance with the limit
table of FIG. 35 and sequence diagram of FIG. 36. The sequence
transitioning processing begins with the asset modification module
30 accessing the limit table (e.g., step 1 of transitioning from
sequence 1). From the limit table, the asset modification module 30
identifies operations to retrieve (e.g., step 2); identify
operation data (e.g., step 3); identify evaluation data (e.g., step
3); and the identify indicators for the identified operations
(e.g., step 3).
[0304] In this example, the asset modification module 30 identified
operations D-O from the limit table (step 2). With reference to
FIG. 36, the sequencing can transition from sequence 1 to sequence
2, 3, or 4 after successful execution of operation B or C of
sequence 1. More specifically, operations D-F are operations of
sequence 2, operations G, H, K, and L are operations of sequence 3,
and operations M, N, and O are operations of sequence 4. In other
words, once operations B or C is executed to open the asset for
modification, the process waits for one of operations D-O to be
triggered to transition to the next sequence.
[0305] For step 3 of transitioning from sequence 1, the asset
modification module identifies the operation data indicators for
the asset (e.g., the asset ID, the data allotment amount, and data
operands, if any). In addition, the asset modification module
identifies evaluation data for analysis and identifies the other
indicators (e.g., trigger indicator, de-trigger indicator, execute
indicator, and pause indicator).
[0306] The processing of transitioning from sequence 1 continues at
step 4 where the asset modification module 30 retrieves the
identified evaluation data and the identified operation data (if
any). The asset modification module 30 analyzes the retrieved
evaluation data in light of the indicators. When a trigger
indicator is met for one of the operations, the operation is
triggered and the status of the operation within the limit table is
updated accordingly. When an activate indicator is met for a
triggered operation, the operation is activated for execution and
the status of the operation within the limit table is updated
accordingly. The asset modification module 30 executes the
activated operation in accordance with the indicators of the limit
table to produce a partial modification resultant (e.g., step
5).
[0307] Depending on which operation is triggered, activated, and
executed, the asset modification process transitions to a next
sequence. For instance, if one of operations D-F is triggered, the
asset modification process transitions to sequence 2, where the
asset is extended. If, however, one of operations G, H, K, or L is
triggered, the asset modification process transitions to sequence
3, where the asset is reduced. Alternatively, if one of operations
M, N, or I is triggered, the asset modification process transitions
to sequence 4, where the asset modification process for the asset
is closed.
[0308] FIG. 40 is a schematic block diagram of another example of
an asset modification module 30 modifying an asset in sequence 4 in
accordance with the limit table of FIG. 35 and sequence diagram of
FIG. 36. With the asset modification process in sequence 4, it can
only transition to sequence 1 (open an asset for modification) or
to sequence 5 (turn off the asset modification process).
[0309] In this sequence state, the asset modification module 30
identified operations B, C, and P from the limit table. Depending
on which operation is triggered, activated, and executed, the asset
modification process transitions to a next sequence. For instance,
if operation P is executed, the asset modification process is
turned off. If, however, operation B or C is executed, the asset
modification process is in a transition state for the asset or for
another asset from which it could transition to sequence 2, 3, or
4.
[0310] FIGS. 41-42 are a diagram of another example of an operation
set limit table for financial portfolio management. The operation
set limit table includes a plurality of columns and a plurality of
rows. A row of the table corresponds to information for a
particular operation and the columns corresponding to the specific
information of the operation. The columns include a sequence field,
an evaluation type field (e.g., evaluation data ID and/or
evaluation data factors), a notes field (optional and for reference
purposes), an activation trigger (e.g., trigger), an execution
trigger (e.g., activate for execution) as shown in FIG. 41 and
includes action field (e.g., operation ID), a % trade allotment
field, a trigger reference field (e.g., data operand), a status
field, and an additional actions field as shown in FIG. 42. Note
that the first two columns in FIG. 42 are the same columns as in
FIG. 41 and are shown for ease of reference.
[0311] FIGS. 43-47 illustrate various examples of performing an
asset modification process in accordance with the limit table of
FIGS. 41 and 42. FIG. 43 includes two graphs of evaluation data
being analyzed for the asset. The top graph illustrates a line
format of the current price, which plots the current price of the
asset in a line format over time, and the bottom graph illustrates
the current price in a candlestick format, which plots a
candlestick format of the current price over time.
[0312] The asset modification module analyzes the evaluation data
in light of the sequence one operations to trigger (i.e., enable)
an asset modification process for the asset. The sequence one
operations include an open initial signal detection, an open
initial position based on moving average, and an open initial
position based on money flow. For the open initial position based
on moving average operation, the asset modification process for an
initial position is triggered (e.g., enabled) when the distance
from the moving average-to-price crossing is at a level indicated
by the trigger and activation indicators. In this example, the
trigger indicator is 10 and the activation indicator is 0,
indicating that when the trigger indicator is met, the operation is
triggered and activated.
[0313] Once the asset modification process is enabled for an
initial position, the asset modification module analyzes the
evaluation data to determine if an operation to open an initial
position is triggered (e.g., a sequence two operation that occurred
after one of the sequence 1 operations is executed). For the
sequence 1 operation, the limit table includes a trigger indicator
of 0 from base level (e.g., the initial price), an execution
indicator of 0, on operation ID of "open first position", a data
allotment indicator of 10%, and a status. Since the trigger and
execution indicators are identical, once the trigger condition is
met, the operation of "open first position" with a 10% allotment at
the current (or initial) price is executed.
[0314] The asset modification module continues to analyze the
evaluation data for another operation to be triggered. At this
point in the execution of the asset modification process, it may
continue at any sequence level. In the example of FIG. 43, the
analysis of the candlestick format of the current price is rising
and triggers the "open limit 2 tops operation" when, as indicated
by the trigger indicator, it is 10 units (e.g., percent, dollars,
etc.) higher than the initial price (e.g., data operand). Since the
activation trigger is 0, it indicates that the operation is to be
activated (i.e., executed) when the trigger condition is met. As
such, at this point, the asset modification module executes the
operation to purchase 25% more of the asset, which now has 35%
open.
[0315] As time continues, the "close limit 3 tops" operation is
triggered when the candlestick format of the current price drops.
The asset modification module executes the operation to sell 25% of
the asset at the current price, leaving 10% of the asset open. When
the candlestick format of the current price again rises, the asset
modification module executes the "open limit 2 tops operation" to
purchase 25% more of the asset, which now has 35% open.
[0316] As time continues, the "open remaining" operation is
triggered when the line format of the current price reaches
1.2*(initial price). Since the activation indicator is 0 for this
operation, the asset modification module executes the operation
upon its triggering to open 100% of the asset, which, for this
stage of the example, equates to purchasing 65% of the asset at the
then current price. The example continues with the asset
modification module executing the "close limit 3 tops" operation a
couple of times perform executing a "close all positions" operation
based on a close all signal.
[0317] FIG. 44 illustrates another example of asset modification
using the line format of the current price. In this example, after
the initial position is opened, the current price drops from the
initial price. The asset modification module analyzes the data for
a triggering condition to be met. When the current price is down 30
from the initial price, the "open limit" operation is triggered
(e.g., trigger indicator is -30). For this operation, the
activation indicator is 20, meaning that the current price needs to
rise 20 from the price at which the operation was triggered before
it is executed. When this occurs, the asset modification module
executes the "open limit" operation to purchase an additional 20%
of the asset.
[0318] FIG. 45 illustrates another example of asset modification
using the line format of the current price and an average position
price. In this example, the analysis of the data is occurring some
time after the initial position is opened (e.g., at time t1) where
there is 50% of the asset open. The asset modification module
analyzes the data for a triggering condition to be met. When the
average position price is up 10 from a given reference point (e.g.,
the initial price), the "close destination stop" operation is
triggered (e.g., trigger indicator is 10). For this operation, the
activation indicator is 20 (e.g., -20 for a close operation),
meaning that the average position price needs to drop 20 from the
average position price at which the operation was triggered before
it is executed. When this occurs, the asset modification module
executes the "close destination stop" operation to sell all open
positions of the asset, which is the 50% that was open in this
example.
[0319] FIG. 46 illustrates another example of asset modification
using the line format of the current price and an average position
price. In this example, the analysis of the data is occurring some
time after the initial position is opened (e.g., at time t1) where
there is 50% of the asset open. The asset modification module
analyzes the data for a triggering condition to be met. When the
average position price is up 40 from a given reference point (e.g.,
the initial price), the "close limit floor" operation is triggered
(e.g., trigger indicator is 40). For this operation, the activation
indicator is 20 (e.g., -20 for a close operation), meaning that the
average position price needs to drop 20 from the average position
price at which the operation was triggered before it is executed.
When this occurs, the asset modification module executes the "close
limit floor" operation to sell 50% of the open positions of the
asset, which leaves 25% of the asset open.
[0320] FIG. 47 illustrates another example of asset modification
using the line format of the current price and an average position
price. In this example, the analysis of the data is occurring some
time after the initial position is opened (e.g., at time t1) where
there is 50% of the asset open. The asset modification module
analyzes the data for a triggering condition to be met. When the
average position price is up 20 from a given reference point (e.g.,
the initial price), the "close limit" operation is triggered (e.g.,
trigger indicator is 20). For this operation, the activation
indicator is 5 (e.g., -5 for a close operation), meaning that the
average position price needs to drop 5 from the average position
price at which the operation was triggered before it is executed.
When this occurs, the asset modification module executes the "close
limit" operation to sell 20% of the open positions of the asset,
which leaves 40% of the asset open.
[0321] FIG. 48 is a diagram of an example of interoperations of
multiple operation set limit tables. In this example, a limit table
may include a row of information that points to another limit
table, to an operation to determine an operation 564, and/or an
operation to determine another limit table 566. The limit table may
further include rows of information that identify particular
operations as previously discussed. For example, limit table 1
includes a row of information that points to an operation pointing
operation 564. The row of information includes indicators (e.g.,
evaluation data, operation data, trigger, and activation) that
enable the operation pointing operation 564 to identify a
particular result operation. As a specific example, assume that
result operation 1 is an operation to purchase a component from a
particular vendor and operation m is an operation to purchase the
component from another vendor. The operation pointing operation 564
executes its operation in light of the indicators to determine
whether one or both of the results operations1 and m should be
selected.
[0322] As another example, limit table 1 includes a row of
information that points to a limit table (LT) pointing operation
566. The row of information includes indicators (e.g., evaluation
data, operation data, trigger, and activation) that enable the LT
pointing operation 566 to identify a particular limit table. As a
specific example, assume that limit table 2 is regarding purchasing
a component based on a first set of evaluation data and limit table
3 is regarding purchasing a component based on a second set of
evaluation data. The LT pointing operation 566 executes its
operation in light of the indicators to determine whether one or
both of the limit tables 2 and 3 should be selected. Note that part
of the operation may be to test each limit table in light of past
evaluation data to determine which one to select.
[0323] As yet another example, limit table 1 includes a row of
information that points to a limit table (LT). The row of
information includes indicators (e.g., evaluation data, operation
data, trigger, and activation) that enable limit table 1 to
identify a particular limit table. As a specific example, assume
that limit table 2 is regarding purchasing a component based on a
first set of evaluation data and limit table 3 is regarding
purchasing a component based on a second set of evaluation data.
Limit table 1 executes the appropriate operation in light of the
indicators to determine whether one or both of the limit tables 2
and 3 should be selected.
[0324] FIG. 49 is a diagram of an example of an asset modification
module implementing an asset modification process via an operation
set limit table that includes rows of information for six
operations (A-F). A trigger indicator analysis module 568 and an
activation indicator analysis module 570 of the asset modification
module are shown. The asset modification module may include other
modules as previously discussed. Each of the operations may be in
one of three states: inactive state 572 (i.e., not triggered),
triggered 574, and executing 576.
[0325] In this example, at time tx, operations A, B, and C are in
the triggered state 574 and operations D, E, and F are in the
inactive state 572. In addition, the trigger indicator analysis
module 568 is analyzing the evaluation data 578 to determine
whether to trigger one or more of operations D, E, and F and the
activation indicator analysis module 570 is analyzing the
evaluation data 578 to determine whether to activate for execution
one or more of operations A, B, and C.
[0326] FIG. 50 is a continuation of the example of FIG. 49 at time
tx +1. At this point in time, the activation indicator analysis
module 570 determines that the evaluation data 578 is exhibiting a
condition that corresponds to an activation indicator for operation
A. As such, operation A is activated for execution on the
corresponding operation data 580. Once operation A has performs its
operation and outputs its result, it is returned to a triggered
state 574.
[0327] FIG. 51 is a continuation of the example of FIGS. 49 &
50 at time tx +2 . At this point in time, the trigger indicator
analysis module 570 determines that the evaluation data 578 is
exhibiting a condition that corresponds to a detriggering indicator
for operation A. As such, operation A is detriggered and placed in
the inactive state 572.
[0328] FIG. 52 is a continuation of the example of FIGS. 49-51 at
time tx +3. At this point in time, each of the trigger and
activation indicator analysis modules 568-570 determines that the
evaluation data 578 is exhibiting a condition that corresponds to a
trigger indicator and an activation indicator, respectively, for
operation D. As such, operation D is activated for execution on the
corresponding operation data 580. Once operation D has performs its
operation and outputs its result, it is returned to a triggered
state 574.
[0329] FIG. 53 is a diagram of another example of an operation set
limit table 212 that includes a plurality of columns and rows of
information corresponding to operations. This limit table is
similar to the limit tables of FIGS. 9 and 34, with the exception
that it does not include a sequence column. As such, when the asset
modification module is performing an asset modification process in
accordance with the limit table, the trigger and activation
indicators for each operation is analyzed with reference to the
appropriate evaluation data. Thus, any one or more of the
operations may be triggered and/or activated at a given time
without reference to what other operations may or may not be
triggered and/or activated. Note that the limit table 212 may
include one or more rows of information that point to another limit
table, to an operation pointing operation, and/or to a limit table
pointing operation as discussed with reference to FIG. 48.
[0330] FIG. 54 is a diagram of an example of differing philosophies
when building an operation set limit table. In this diagram, the
horizontal axis corresponds to an approach to analyzing the
evaluation data 582, where, closer to the origin, corresponds to a
more aggressive approach. The positive vertical axis corresponds to
a favorable analysis 584 (e.g., the evaluation data is indicating
conditions are favorable to increase an asset), where, the further
from the origin, the more favorable. The negative vertical axis
corresponds to an unfavorable analysis 586 e.g., the evaluation
data is indicating conditions are unfavorable to increase an
asset), where, the further from the origin, the more
unfavorable.
[0331] In general, the analysis of the evaluation data 582
includes, but is not limited to, detecting predictable patterns,
trends, factors, values, etc. of the current evaluation data. The
level of favorability (or unfavorability) is how closely the
detected patterns, trends, factors, values, etc. match past
patterns, trends, factor values, etc., the predictability of the
asset modification outcome therefrom, and the likelihood that
evaluation data will continue to follow past trends, patterns,
factors, values, etc. For example, when the analysis yields
patterns, trends, etc. that don't particular match the predictable
patterns, trends, etc., the favorability may be indeterminate 588.
The analysis may also be indeterminate 588 if the pattern, trend,
etc. are corresponding to predictable patterns, trends, etc., but
the outcome of asset modification from these patterns is mixed
(e.g., 50% of the time, the asset grows, 30% of the time the asset
decreases, and 20% of the time the asset remains relatively
constant). The analysis may further be indeterminate if the
pattern, trend, etc. are corresponding to predictable patterns,
trends, etc., but the likelihood that the evaluation data will
continue to follow the trends, patterns, etc. is mixed (e.g., some
times it does continue and other times it does not).
[0332] As the ability to calculate a favorable or unfavorable
analysis, the more the curve moves from the origin (e.g., the
indeterminate state). Even though the analysis may be
indeterminate, a limit table may include one or more operations to
modify an asset in this state of analysis. The amount of an asset
to expose to modification and what indicators to set for triggering
and activating the modification process will vary depending on the
aggressiveness of the approach being taken. The more aggressive the
approach, the more of the asset will be exposed and the lower the
thresholds for the indicators will be.
[0333] When the analysis is favorable, the level of favorability
584 may be used to determine how much of the asset to expose for
modification and the threshold for the indicators. For instance,
when the pattern, trend, etc. matching is good, the predictability
is good, and the likelihood of continuing is good, then may want to
take an aggressive approach and subject a majority of the asset to
modification. Conversely, when the analysis is unfavorable 586, may
want to take a less aggressive approach to minimize the asset to
modification.
[0334] FIG. 55 is a diagram of an example of historical evaluation
data table 590 for building an operation set limit table. The table
includes fields for evaluation data ID 592, characteristics 594,
recognition filter parameters 596, frequency 598, frequency
deviation 600, factors 602, subsequent characteristics 604, and
probability of subsequent characteristics 606. Each row corresponds
to a particular characteristic of one or more evaluation data. An
evaluation data may be identified in multiple rows for different
characteristics.
[0335] For example, evaluation data 1 is being analyzed for pattern
a using two different recognition filter parameters. Using these
parameters, the pattern occurs about three times per day for the
last month with a daily deviation of 0.65 times per day. For
parameter set 1, the factors include that this pattern is not
preceded by pattern .beta.. Under these conditions, trend B will
occur within a given time frame (e.g., factions of a second,
seconds, minutes, hours, days, etc.) with a probability of 67%
(e.g., for the number of times these conditions have been monitors,
trend B follows pattern .alpha. within the time frame 67% of the
time). For parameter set 2, the factors include that this pattern
is preceded by pattern .beta.. Under these conditions, trend A will
occur nearly immediately (e.g., factions of a second, seconds,
etc.) with a probability of 80%.
[0336] As another example, evaluation data 2 is being analyzed for
pattern .phi., trend A, and a three-month minimum value using
different parameters. For the time the evaluation data has been
analyzed, which could be days, weeks, months, etc., pattern .phi.
occurs about 7 times per day for the last two weeks and trend A has
occurred once per day for the last five weeks. Pattern .phi. has a
daily deviation of 3.5 and trend A has a daily deviation of 0.15.
The factor for pattern .phi. is that current evaluation data has to
exceed a value of M; there are no factors for trend A. When these
conditions are met for pattern .phi., pattern .psi. is the
subsequent pattern about 40% of the time, pattern is the subsequent
pattern about 30% of the time, and the remaining 30% of the time
there is no discernible characteristic. When the three-month
minimum value is detected, trend C occurs within 2 days about 80%
of the time.
[0337] The last entry in the table identifies evaluation data 4, 5,
and 6 with a characteristic of function 1. The function may be a
logic function, a mathematical function, a comparative function, a
translation function, a scaling function, a mapping function, a
filtering function, a combination thereof, etc. The analysis
provided favorable results with a frequency of 2 times per day for
the last 8 weeks with a daily deviation of 0.35. There are no
factors and the subsequent characteristic is pattern Q of
evaluation data 4 within K minutes at a probability of 35%. In
practice, the evaluation data being analyzed, the characteristics,
the parameters, the frequency, the frequency deviation, factors,
subsequent characteristics, and probability may be in an almost
endless combination and range of results.
[0338] FIG. 56 is a schematic block diagram of an embodiment of an
evaluation filter of an asset modification module that may be used
to build a limit table. The evaluation filter includes a buffer
608, a time scaler 610, an amplitude scaler 612, a recognition
filter 614, and an analyzer 616. The buffer 608 is of sufficient
size to store a sample set of the evaluation data being analyzed.
The time scaler 610 and amplitude scaler 612 function in accordance
with the recognition filter parameters 618 to scale the samples of
the evaluation data to fit within a sample window of a reference
pattern 624. FIG. 57 is a diagram of an example of a reference
pattern 624 that is within a sample window having a time frame of
TO and an amplitude range of A0.
[0339] The recognition filter 614, which may be a matching filter,
a convolution filter, etc., filters the scaled evaluation data
samples for one or more recognizable characteristics (e.g., trends,
patterns, values, function result, etc.) in light of the
recognition filter parameters 618 (e.g., a reference pattern, a
reference trend, a reference value, a reference function result,
etc.). The analyzer 616 analyzes the filters output for how closely
the evaluation data (e.g., as shown in FIG. 58) matches the
reference characteristic to produce a recognition output 622. The
more closely the filtered evaluation data matches the reference
pattern 624, the more favorable the analysis. In addition, the
analyzer 616 may provide feedback 620 to adjust the time and/or
amplitude scaling to adjust the evaluation data sample 626.
[0340] FIG. 59 is a schematic block diagram of an example of
operation of an evaluation data recognition filter 614. In this
example, the evaluation data sample is at various rates and/or
magnitudes 628. As the time window 630 scans the evaluation data
sample, the recognition filter 614 is comparing it to a reference
characteristic (e.g., a reference pattern 624).
[0341] FIG. 60 illustrates the output of the recognition filter 632
as the time window 630 scans the evaluation data sample. For a
majority of the sample time window 630, the output of the filter is
low. The analyzer 616 analyzes the output with reference to
thresholds (e.g., no match, possible match, probable match, likely
match, etc.). When the output is below the no match threshold 634,
the analyzer 616 provides no feedback and keeps the time window 630
scanning the evaluation data sample.
[0342] When the output of the filter is above the no match
threshold 634, the analyzer 616 has some decisions to make. First,
it decides what other thresholds the output exceeds. If it exceeds
the likely match threshold 640, the analyzer marks the current time
window position of the evaluation data sample to indicate a likely
match to the reference characteristic. In this instance, the
analyzer 616 continues analyzing the output of the filter for the
reference characteristic to occur again. Note that the filter may
be analyzing the evaluation data with respect to multiple reference
characteristics. In this instance, when the output exceeds the no
match threshold, the analyzer determines whether another threshold
is exceeded and for what reference characteristic.
[0343] When the output of the filter is at a level above the no
match threshold 634 and below the likely match threshold 640, the
analyzer 616 may provide feedback to adjust the evaluation data
sample to determine if a better indication of a match can be
achieved. If yes, the analyzer 616 includes that in the recognition
output 632. If not, the analyzer 616 includes the initial, or more
favorable, match outcome as the recognition output.
[0344] FIGS. 61-63 are a logic diagram of a method for building a
limit table that may be executed by recognition filter module. The
method begins with the module providing a sliding time window of an
evaluation data sample to the recognition filter without adjustment
to the time scale or the amplitude scale 642. The method continues
with the module providing recognition filter parameters regarding a
reference characteristic to the recognition filter 644. The method
continues with the module analyzing the recognition filter output
646.
[0345] The method branches in accordance with the analysis of the
recognition filter output 648. When the match indicator indicates a
likely match, the evaluation data history table is updated 650.
When the match indicator indicates no match, the method continues
with the module determining whether the time and/or amplitude
scaling variations have been exhausted 652. For example, may allow
for scaling from 50% to 200% of the original evaluation data
sample. If the variations are exhausted, the method is ended for
the current analysis, but repeats for further analysis of
evaluation data samples.
[0346] If the variations are not exhausted, the method continues
with the module adjusting the time and/or amplitude of the
evaluation data sample 654. The method continues with the module
providing the adjusted evaluation data sample to the recognition
filter and the method repeats as shown 656.
[0347] When the match indicator indicates a possible match, the
method continues in FIG. 62 where the module adjusts the time
and/or amplitude of the evaluation data sample 658. The method
continues with the module providing the adjusted evaluation data
sample to the recognition filter 660. The method continues with the
module providing a sliding time window to the recognition filter
for the adjusted evaluation data sample. The method continues with
the module providing recognition filter parameters regarding a
reference characteristic to the recognition filter. The method
continues with the module analyzing the recognition filter output
662.
[0348] The method continues with the module analyzing the output of
the recognition filter for a better match 664. If a better match is
not indicated, the method continues with the module determining
whether the time and/or amplitude scaling variations have been
exhausted 666. If the variations are exhausted, the module updates
the historical evaluation data table based on the results of the
possible match 668. If the variations are not exhausted, the method
continues with the module adjusting the time and/or amplitude of
the evaluation data sample 670. The method continues with the
module providing the adjusted evaluation data sample to the
recognition filter and the method repeats as shown.
[0349] When the analysis indicates a better match, the method
continues with determining whether the better match is a likely
match 672. If yes, the method continues with the module updating
the historical evaluation data table based on the results of the
likely match 674. If the better match is not a likely match, the
method continues with the module determining whether the better
match is a probable match 676. If not, the method repeats as shown.
If yes, the method continues on FIG. 63.
[0350] In FIG. 63, when the match indicator indicates a probably
match, the method continues with the module adjusting the time
and/or amplitude of the evaluation data sample 678. The method
continues with the module providing the adjusted evaluation data
sample to the recognition filter. The method continues with the
module providing a sliding time window to the recognition filter
for the adjusted evaluation data sample. The method continues with
the module providing recognition filter parameters regarding a
reference characteristic to the recognition filter 680. The method
continues with the module analyzing the recognition filter output
682.
[0351] The method continues with the module analyzing the output of
the recognition filter for a better match 684. If a better match is
not indicated, the method continues with the module determining
whether the time and/or amplitude scaling variations have been
exhausted 686. If the variations are exhausted, the module updates
the historical evaluation data table based on the results of the
probable match 688. If the variations are not exhausted, the method
continues with the module adjusting the time and/or amplitude of
the evaluation data sample 690. The method continues with the
module providing the adjusted evaluation data sample to the
recognition filter and the method repeats as shown.
[0352] When the analysis indicates a better match, the method
continues with determining whether the better match is a likely
match 692. If yes, the method continues with the module updating
the historical evaluation data table based on the results of the
likely match 694. If the better match is not a likely match, the
method repeats as shown.
[0353] FIG. 64 is a logic diagram of another method that may be
executed by an asset modification module or other processing module
to build a limit table. The method begins with the module obtaining
limit table (LT) configuration criteria, which relates to desired
attributes of the limit table 696. The attributes include, but are
not limited to, risk level, evaluation data relevancy, asset
modification philosophy, reliability level (e.g., proven, unproven,
works some times, etc.), favorable evaluation data patterns and/or
trends, performance information, etc.
[0354] The method continues with the module determining asset
modification objectives based on the LT configuration criteria 698.
The asset modification objectives include, but are not limited to,
grow asset, gather information, manage asset use, manage asset
distribution, identify use opportunities, identify sales
opportunities, and identify acquisition opportunities.
[0355] The method continues with the module selecting evaluation
data of interest based on the asset modification objectives 700.
The method continues with the module determining evaluation data
factors (e.g., type of asset, modification timing (e.g., time of
day, inventory depletion, etc.), evaluation data sources of
interest availability, evaluation data analysis (e.g., pattern
mapping, trend detection, value thresholds, comparative analysis,
etc.)) 702. The method continues with the module selecting
characteristics of the selected evaluation data to monitor based on
the evaluation data and the asset modification objectives 704.
These three steps may be based on default settings, starting with a
large number of evaluation data and narrowing via these steps, user
inputs, a look up table, subscription to evaluation data sources,
trial and error, etc.
[0356] The method branches into three parallel branches. In the
parallel branches, the module determines activation indicators
based on the selected characteristics 706, determines trigger
indicators based on the selected characteristics 708, and
determines operation data indictors based on the asset modification
objectives and the selected characteristics 710.
[0357] The method continues with the module creating a limit table
entry based on the foregoing 712. The method continues with the
module determining whether to select another characteristic 714. If
yes, the method repeats as shown. If not, the method is
complete.
[0358] FIG. 65 is a schematic block diagram of an example of an
assembly line of building a product 716. In this example, the
product 716 is comprised of a plurality of components (1, . . . ,
X, Y, . . . , and .beta.). At least some of the components are
comprised of subcomponents. For example, component 1 is comprised
of sub-components 1-1 through 1-x, which is typically manufactured
by a different entity than the entity manufacturing the product. In
addition, some of the sub-components may be comprised of further
sub-components. For example, sub-component 1-1 is comprised of
further sub-components 1-1-1 through 1-1-y.
[0359] The availability of a component, sub-component, or further
sub-components affects an inventory philosophy for manufacturing
the product. For example, if one sub-component or further
sub-component becomes in short supply, the production of the
product may be adversely affected. As such, it would behoove the
manufacturer to monitor evaluation data that affects the
availability of its components, sub-components, and further
sub-components. The evaluation data may include pricing
information, component availability, raw materials availability,
raw material supply issues, shipping requirements and/or
constraints, assembly requirement information (e.g., timing,
complexity, labor involvement, etc.), import/export regulations,
political issues, labor issues, weather, component and/or
sub-component demand, product marketing information, etc.
[0360] FIG. 66 is a diagram of an example of limit tables 718 for
inventory management of an assembly line of building a product of
FIG. 65. As shown, a plurality of limit tables 718 may be created
for managing inventory for the product, each of which includes
various operations that, when executed, produces product operation
data analysis 720, which may include, for a given time frame (e.g.,
a day, a week, a month, etc.), buy more of one or more components,
buy less of one or more components, buy at status quo for one or
more components, user alterative suppliers for one or more
components, increase inventory of one or more components for the
next time frame, decrease inventory of one or more components for
the next time frame, timing for placing orders for one or more
components, quantity per order, determine shipping options for one
or more components, etc.
[0361] The execution of a selected limit table 718 is based on the
evaluation data regarding the product 722 and the operation data
analysis for at least some of the components. In addition, the
selection of a limit table 718 may be based on the operation data
analysis of one or more of the components. Executing the operations
of a selected limit table 718 produces the component operation data
analysis for a component.
[0362] FIG. 67 is a diagram of an example of limit tables 718 for
inventory management of a component of a product of FIG. 65. This
example illustrates the tiers of operation data analysis for
component 1 being built on the operation data analysis of its
sub-components, which is built on the operation data analysis of
its sub-components. By using g a tiered analysis approach to
inventory management, inventory supplies can be adjusted based on
issues that affect the availability and pricing of components. For
example, historical analysis of politics in a particular country
shows that, when a particular political issue arises, the
availability of raw materials for a sub-component is reduced within
6 months of the outbreak of the political issue. As such, it might
be prudent to overstock the component now to avoid potential
limited supplies in the future.
[0363] FIG. 68 is a diagram of an example of inventory management
of an assembly line of building a product by monitoring various
evaluation data for component .psi.. The evaluation data includes
evaluation data 1, evaluation data 2, evaluation data .lamda.,
pricing information, and supply to demand ratio. The grey areas in
the past section are reflective of when the data is exhibiting a
recognizable characteristic (e.g., pattern, trend, value, function
result, etc.). Note that the time frame may be seconds, minutes,
hours, days, weeks, months, years, etc.
[0364] The various evaluation data may be analyzed individually,
collectively, or in any combination for one or more
characteristics. For example, characteristic 1 may be a pattern of
evaluation data .lamda. that has an increasing value for a period
of time at a desired slope. As another example, characteristic 2
may be a function executed upon evaluation data 1 and 2. As yet
another example, characteristic 3 may based on a trend of the price
evaluation data. As a further example, the characteristic 4 may be
based on a functional result of all of the evaluation data.
[0365] The identifiable characteristics of the past are used to
identify similar characteristics in the future, which can be used
to set indicators for a limit table. Such limit tables may then be
used to manage inventory based on recognizable characteristics and
probable subsequent characteristics.
[0366] FIG. 69 is a diagram of an example of graphical user
interface (GUI) for an asset modification process. The GUI includes
a plurality of windows. Some of the windows may be used to display
a graphical representation of evaluation data of interest. One or
more other windows may be used to illustrate the processing of a
limit table 724. Another window may be used to illustrate a
graphical representation of the operation data (e.g., asset
modification) performance (e.g., increasing, reducing, etc.) 726.
Another window may be used for a graphical representation of a
control panel 728.
[0367] The control panel 728 may include buttons for stopping the
execution of a limit table 730, adding another asset to the
processing of the current limit table 732, removing an asset from
the processing of the current limit table 734, changing the limit
table 736, selecting a new asset and a new limit table for
modification 738. Other controls may be included to build a limit
table, selecting a limit table, selecting an asset, etc.
[0368] As may be used herein, the terms "substantially" and
"approximately" provides an industry-accepted tolerance for its
corresponding term and/or relativity between items. Such an
industry-accepted tolerance ranges from less than one percent to
fifty percent and corresponds to, but is not limited to, component
values, integrated circuit process variations, temperature
variations, rise and fall times, and/or thermal noise. Such
relativity between items ranges from a difference of a few percent
to magnitude differences. As may also be used herein, the term(s)
"operably coupled to", "coupled to", and/or "coupling" includes
direct coupling between items and/or indirect coupling between
items via an intervening item (e.g., an item includes, but is not
limited to, a component, an element, a circuit, and/or a module)
where, for indirect coupling, the intervening item does not modify
the information of a signal but may adjust its current level,
voltage level, and/or power level. As may further be used herein,
inferred coupling (i.e., where one element is coupled to another
element by inference) includes direct and indirect coupling between
two items in the same manner as "coupled to". As may even further
be used herein, the term "operable to" or "operably coupled to"
indicates that an item includes one or more of power connections,
input(s), output(s), etc., to perform, when activated, one or more
its corresponding functions and may further include inferred
coupling to one or more other items. As may still further be used
herein, the term "associated with", includes direct and/or indirect
coupling of separate items and/or one item being embedded within
another item. As may be used herein, the term "compares favorably",
indicates that a comparison between two or more items, signals,
etc., provides a desired relationship. For example, when the
desired relationship is that signal 1 has a greater magnitude than
signal 2, a favorable comparison may be achieved when the magnitude
of signal 1 is greater than that of signal 2 or when the magnitude
of signal 2 is less than that of signal 1.
[0369] As may also be used herein, the terms "processing module",
"processing circuit", and/or "processing unit" may be a single
processing device or a plurality of processing devices. Such a
processing device may be a microprocessor, micro-controller,
digital signal processor, microcomputer, central processing unit,
field programmable gate array, programmable logic device, state
machine, logic circuitry, analog circuitry, digital circuitry,
and/or any device that manipulates signals (analog and/or digital)
based on hard coding of the circuitry and/or operational
instructions. The processing module, module, processing circuit,
and/or processing unit may be, or further include, memory and/or an
integrated memory element, which may be a single memory device, a
plurality of memory devices, and/or embedded circuitry of another
processing module, module, processing circuit, and/or processing
unit. Such a memory device may be a read-only memory, random access
memory, volatile memory, non-volatile memory, static memory,
dynamic memory, flash memory, cache memory, and/or any device that
stores digital information. Note that if the processing module,
module, processing circuit, and/or processing unit includes more
than one processing device, the processing devices may be centrally
located (e.g., directly coupled together via a wired and/or
wireless bus structure) or may be distributedly located (e.g.,
cloud computing via indirect coupling via a local area network
and/or a wide area network). Further note that if the processing
module, module, processing circuit, and/or processing unit
implements one or more of its functions via a state machine, analog
circuitry, digital circuitry, and/or logic circuitry, the memory
and/or memory element storing the corresponding operational
instructions may be embedded within, or external to, the circuitry
comprising the state machine, analog circuitry, digital circuitry,
and/or logic circuitry. Still further note that, the memory element
may store, and the processing module, module, processing circuit,
and/or processing unit executes, hard coded and/or operational
instructions corresponding to at least some of the steps and/or
functions illustrated in one or more of the Figures. Such a memory
device or memory element can be included in an article of
manufacture.
[0370] The present invention has been described above with the aid
of method steps illustrating the performance of specified functions
and relationships thereof. The boundaries and sequence of these
functional building blocks and method steps have been arbitrarily
defined herein for convenience of description. Alternate boundaries
and sequences can be defined so long as the specified functions and
relationships are appropriately performed. Any such alternate
boundaries or sequences are thus within the scope and spirit of the
claimed invention. Further, the boundaries of these functional
building blocks have been arbitrarily defined for convenience of
description. Alternate boundaries could be defined as long as the
certain significant functions are appropriately performed.
Similarly, flow diagram blocks may also have been arbitrarily
defined herein to illustrate certain significant functionality. To
the extent used, the flow diagram block boundaries and sequence
could have been defined otherwise and still perform the certain
significant functionality. Such alternate definitions of both
functional building blocks and flow diagram blocks and sequences
are thus within the scope and spirit of the claimed invention. One
of average skill in the art will also recognize that the functional
building blocks, and other illustrative blocks, modules and
components herein, can be implemented as illustrated or by discrete
components, application specific integrated circuits, processors
executing appropriate software and the like or any combination
thereof.
[0371] The present invention may have also been described, at least
in part, in terms of one or more embodiments. An embodiment of the
present invention is used herein to illustrate the present
invention, an aspect thereof, a feature thereof, a concept thereof,
and/or an example thereof. A physical embodiment of an apparatus,
an article of manufacture, a machine, and/or of a process that
embodies the present invention may include one or more of the
aspects, features, concepts, examples, etc. described with
reference to one or more of the embodiments discussed herein.
Further, from figure to figure, the embodiments may incorporate the
same or similarly named functions, steps, modules, etc. that may
use the same or different reference numbers and, as such, the
functions, steps, modules, etc. may be the same or similar
functions, steps, modules, etc. or different ones.
[0372] Unless specifically stated to the contra, signals to, from,
and/or between elements in a figure of any of the figures presented
herein may be analog or digital, continuous time or discrete time,
and single-ended or differential. For instance, if a signal path is
shown as a single-ended path, it also represents a differential
signal path. Similarly, if a signal path is shown as a differential
path, it also represents a single-ended signal path. While one or
more particular architectures are described herein, other
architectures can likewise be implemented that use one or more data
buses not expressly shown, direct connectivity between elements,
and/or indirect coupling between other elements as recognized by
one of average skill in the art.
[0373] The term "module" is used in the description of the various
embodiments of the present invention. A module includes a
processing module, a functional block, hardware, and/or software
stored on memory for performing one or more functions as may be
described herein. Note that, if the module is implemented via
hardware, the hardware may operate independently and/or in
conjunction software and/or firmware. As used herein, a module may
contain one or more sub-modules, each of which may be one or more
modules.
[0374] While particular combinations of various functions and
features of the present invention have been expressly described
herein, other combinations of these features and functions are
likewise possible. The present invention is not limited by the
particular examples disclosed herein and expressly incorporates
these other combinations.
* * * * *