U.S. patent application number 10/007434 was filed with the patent office on 2002-04-18 for system, method and apparatus for demand-initiated intelligent negotiation agents in a distributed network.
Invention is credited to Solomon, Neal.
Application Number | 20020046157 10/007434 |
Document ID | / |
Family ID | 27358365 |
Filed Date | 2002-04-18 |
United States Patent
Application |
20020046157 |
Kind Code |
A1 |
Solomon, Neal |
April 18, 2002 |
System, method and apparatus for demand-initiated intelligent
negotiation agents in a distributed network
Abstract
In a distributed communications environment, intelligent
negotiation agents (INAS) are described. INAs are autonomous
intelligent software agents that negotiate for the acquisition of
products, services and bundles by adopting roles of buying, selling
and brokering in which a buyer agent negotiates with at least two
seller agents. In order to automate INAs, artificial intelligence
technologies, including neural networks, genetic algorithms and
genetic programming, are applied. AI allows automous software
agents to adapt to changing markets and allows the INAs to use
mobility in a distributed system. Multi-session auction approaches
based on initial parameters and self-motivated adaptation are used
by buyer and seller INAs. Negotiation factors include multilateral
and multivariate parameters as well as price. Accountability data,
marketing promotions, risk management options and made-to-order
services are integrated into this system. Search agents initiate
the negotiation process. Analytical agents inform INAs throughout
the negotiation process. Transaction agents close and track
transactions. Micro-agents are used for buyer INAs to interact
simultaneously with two or more seller INAs. Dynamic mobile
negotiation agents (D-INAs) operate as double agents that alternate
roles between buyer and seller; such adaptive roles allow arbitrage
functions.
Inventors: |
Solomon, Neal; (Oakland,
CA) |
Correspondence
Address: |
Neal Solomon
P.O. Box 21297
Oakland
CA
94620
US
|
Family ID: |
27358365 |
Appl. No.: |
10/007434 |
Filed: |
December 3, 2001 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
|
60162932 |
Nov 1, 1999 |
|
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60250819 |
Dec 1, 2000 |
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Current U.S.
Class: |
705/37 ;
705/26.1; 719/317 |
Current CPC
Class: |
G06Q 30/0601 20130101;
G06Q 50/188 20130101; G06Q 40/04 20130101; G06Q 30/08 20130101;
G06Q 30/02 20130101; G06Q 30/0224 20130101 |
Class at
Publication: |
705/37 ; 705/27;
709/317 |
International
Class: |
G06F 017/60; G06F
009/44 |
Claims
I claim:
1. A system for automated negotiation for procurement of an item
using computers that communicate over a distributed network, the
system comprising: a buyer's intelligent negotiation agent for
sending and receiving information regarding at least one selected
item to and from a plurality of sellers' intelligent negotiation
agents, said selected item being one of a group of individual
product items and individual service items, and said at least one
of a plurality of sellers' intelligent negotiation agents for
sending and receiving information regarding said selected item to
and from said buyer's intelligent negotiation agent, wherein, when
said buyer's intelligent negotiation agent receives a response to a
buyer's initial query regarding said selected item from at least
one of said plurality of sellers' intelligent negotiation agents,
said buyer's intelligent negotiation agent engages in a negotiation
with each of said plurality of sellers' intelligent negotiation
agents for procurement of said selected item.
2. The system for automated negotiation for procurement of an item
of claim 1, wherein: said item comprises a preset bundle of
items.
3. The system for automated negotiation for an item procurement of
claim 1, wherein: said buyer's initial query comprises a request
for bids to sell said selected item.
4. The system for automated negotiation for an item procurement of
claim 1, wherein: said response comprises a bid to sell said
selected item.
5. The system of claim 1 wherein: said response comprises a bid to
sell said selected item, said bid comprising a set of seller's
specifications, and said negotiation comprises said buyer's
intelligent negotiation agent comparing each of said seller's
specifications against a set of buyer's specifications, and
selecting an optimal set of new buyer's specifications in view of a
preprogrammed buyer strategy.
6. Th e system of claim 5 wherein: said preprogrammed buyer
strategy comprises price minimization.
7. The system of claim 5 wherein: said preprogrammed buyer strategy
comprises long-term relationship preservation.
8. The system of claim 5 wherein: said preprogrammed buyer strategy
comprises matching of interests with said seller.
9. The system of claim 5 wherein: said preprogrammed buyer strategy
comprises position justification and argumentation.
10. The system of claim 5 wherein: said preprogrammed buyer
strategy comprises a deterrence approach.
11. The system of claim 5 wherein: said preprogrammed buyer
strategy comprises anticipation of said seller's position.
12. The system for automated negotiation for an item procurement of
claim 1, wherein: said initial query comprises a set of buyer
specifications for said selected item, and said response to said
initial query comprises a set of seller's specifications for said
selected item, wherein said negotiation comprises said buyer's
intelligent negotiation agent developing a new set of buyer
specifications responsive said seller's specifications for
transmission to said one of said sellers' intelligent negotiation
agents.
13. The system of claim 12 wherein: said negotiation comprises said
one sellers' intelligent negotiation agent developing a new set of
seller's specifications responsive to said new set of buyer's
specifications for transmission to said buyer's intelligent
negotiation agent.
14. The system of claim 13 wherein: said developing said new set of
seller's specifications comprises comparing each of said new set of
buyer's specifications against a set of seller's specifications,
and selecting an optimal set of new seller's specifications in view
of a preprogrammed seller strategy.
15. The system of claim 14 wherein: said preprogrammed seller
strategy comprises price maximization.
16. The system of claim 14 wherein: said preprogrammed seller
strategy comprises long-term relationship preservation.
17. The system of claim 14 wherein: said preprogrammed seller
strategy comprises matching of interests with said buyer.
18. The system of claim 14 wherein: said preprogrammed seller
strategy comprises position justification and argumentation.
19. The system of claim 14 wherein: said preprogrammed seller
strategy comprises a deterrence approach.
20. The system of claim 14 wherein: said preprogrammed seller
strategy comprises anticipation of said buyer's next position.
21. The system of claim 12 wherein: said buyer's intelligent
negotiation agent receives a constantly changing stream of
information from a plurality of market databases, and said buyer's
intelligent negotiation agent evolves in response to said stream of
information for seeking an optimal match for each specification of
said set of buyer's specifications in said bids from said sellers'
intelligent negotiation agents.
22. The system of claim 21 wherein: second-order rules specify
limits for said new set of buyer's specifications as said buyer's
intelligent negotiation agent evolves.
23. The system of claim 1, wherein: said negotiation comprises said
buyer's intelligent negotiation agent transmitting an acceptance of
the bid from one of said sellers' intelligent negotiation
agents.
24. The system of claim 23, further comprising: an intelligent
transaction agent in communication with said buyer's intelligent
negotiation agent, said intelligent transaction agent for
autonomously clearing transaction terms to complete a transaction
for procurement of said selected item responsive to said acceptance
of said bid.
25. The system of claim 1, wherein: said negotiation comprises said
buyer's intelligent negotiation agent transmitting a counter-offer
to at least one of said sellers' intelligent negotiation agents
responsive to said bid.
26. The system of claim 25, wherein: said counter-offer comprises a
set of buyer's item specifications.
27. The system of claim 1, wherein: said buyer's intelligent
negotiation agent receives said bid from at least eight of said
sellers' intelligent negotiation agents, and said buyer's
intelligent negotiation agent selects only four of said sellers'
intelligent negotiation agents with which to continue said
negotiation.
28. The system of claim 1, wherein: said buyer's intelligent
negotiation agent receives said bid from at least four of said
sellers' intelligent negotiation agents, and said buyer's
intelligent negotiation agent selects only two of said sellers'
intelligent negotiation agents with which to continue said
negotiation.
29. The system of claim 28, wherein: said buyer's intelligent
negotiation agent selects one of said two of said sellers'
intelligent negotiation agents with which to continue said
negotiation.
30. The system of claim 1, wherein: said buyer's intelligent
negotiation agent selects only one of said sellers' intelligent
negotiation agents with which to continue said negotiation.
31. The system of claim 1, wherein: said bid comprises a set of
seller's item specifications.
32. The system of claim 31, further comprising: an intelligent
transaction agent for autonomously verifying said set of seller's
item specifications.
33. The system of claim 1, further comprising: a set of buyer's
item specifications, and a set of seller's item specifications,
wherein said negotiation comprises said buyer's intelligent
negotiation agent seeking an optimal match from said sellers'
intelligent negotiation agents for each of said set of buyer's item
specifications, and said negotiation further comprises said
sellers' intelligent negotiation agents seeking an optimal match
from said buyer's intelligent negotiation agent for each of said
set of seller's item specifications.
34. The system of claim 1, further comprising: an intelligent
transaction agent in communication with at least one of said
buyer's intelligent negotiation agent and said sellers' intelligent
negotiation agents, said intelligent transaction agent for
autonomously clearing transaction terms to complete a transaction
for procurement of said selected item.
35. The system of claim 34, wherein: said intelligent transaction
agent is in communication with said buyer's intelligent negotiation
agent.
36. The system of claim 34, wherein: said intelligent transaction
agent is in communication with one of said seller's intelligent
negotiation agents.
37. The system of claim 1, wherein: said buyer's intelligent
negotiation agent includes a preprogrammed set of buyer's
specifications, said buyer's intelligent negotiation agent receives
a constantly changing stream of information from a plurality of
market databases, and said buyer's intelligent negotiation agent
evolves in response to said stream of information for seeking an
optimal match for each specification of said set of buyer's
specifications in said bids from said sellers' intelligent
negotiation agents.
38. The system of claim 37, wherein: said response comprises a bid
to sell said selected item, said bid comprising a set of seller's
specifications, and said buyer's intelligent negotiation agent
compares each of said seller's specifications against a set of
buyer's specifications, and selects an optimal set of new buyer's
specifications in view of a preprogrammed buyer strategy.
39. The system of claim 37, wherein: said buyer's specifications
include item specifications for said selected item.
40. The system of claim 37, wherein: said buyer's specifications
include transaction specifications for a transaction for
procurement of said selected item.
41. The system of claim 37, wherein: said buyer's intelligent
negotiation agent selects one of a plurality of evolutionary
computation resources to perform said negotiation.
42. The system of claim 41, wherein: said plurality of evolutionary
computation resources comprises genetic algorithms.
43. The system of claim 41, wherein: said plurality of evolutionary
computation resources comprises genetic programming.
44. The system of claim 41, wherein: said plurality of evolutionary
computation resources comprises neural networks.
45. The system of claim 41, wherein: said buyer's intelligent
negotiation agent selects one of said plurality of evolutionary
computation resources for optimal computation of said match for
each specification of said set of buyer's specifications in said
bids from said sellers' intelligent negotiation agents.
46. The system of claim 19, wherein: said buyer's intelligent
negotiation agent compares each of a set of seller's specifications
against said set of buyer's specifications, and selects an optimal
set of new buyer's specifications in view of a preprogrammed buyer
strategy.
47. The system of claim 1, wherein: said seller's intelligent
negotiation agent includes a preprogrammed set of seller's
specifications, said seller's intelligent negotiation agent
receives a constantly changing stream of information from a
plurality of market databases, and said seller's intelligent
negotiation agent evolves in response to said stream of information
for seeking an optimal match from said buyer's intelligent
negotiation agents for each of said set of seller's
specifications.
48. The system of claim 47, wherein: second-order rules specify
limits for said new set of seller's specifications as said seller's
intelligent negotiation agent evolves.
49. The system of claim 47, wherein: said seller's intelligent
negotiation agent compares each of said buyer's specifications
against a set of seller's specifications, and selects an optimal
set of new seller's specifications in view of a preprogrammed buyer
strategy.
50. The system of claim 47, wherein: said seller's specifications
include item specifications for said selected item.
51. The system of claim 47, wherein: said seller's specifications
include transaction specifications for a transaction for
procurement by a buyer of said selected item.
52. The system of claim 47, wherein: said seller's intelligent
negotiation agent selects one of a plurality of evolutionary
computation resources to perform said negotiation.
53. The system of claim 52, wherein: said plurality of evolutionary
computation resources comprises genetic algorithms.
54. The system of claim 52, wherein: said plurality of evolutionary
computation resources comprises genetic programming.
55. The system of claim 52, wherein: said plurality of evolutionary
computation resources comprises neural networks.
56. The system of claim 52, wherein: said seller's intelligent
negotiation agent selects one of said plurality of evolutionary
computation resources for optimal computation of said match for
each specification of said set of seller's specifications.
57. The system of claim 1, wherein: said bid comprises a discounted
price for said selected item.
58. The system of claim 1, wherein: said bid comprises an option to
upgrade features of said selected item.
59. The system of claim 1, wherein: said bid comprises an option to
include additional services related to said selected item.
60. The system of claim 1, wherein: said bid comprises a quantity
price discount for said selected item.
61. The system of claim 1, wherein: said bid comprises financing
for procurement of said selected item.
62. The system of claim 1, wherein: said bid comprises
warranties.
63. The system of claim 1, wherein: said bid comprises
insurance.
64. The system of claim 1, wherein: said bid comprises a proximity
marketing discount.
65. The system of claim 1, wherein: said bid comprises a yield
management promotion.
66. The system of claim 1, wherein: said bid comprises a technology
decay promotion.
67. The system of claim 1, wherein: said bid comprises at least one
contract contingency authorizing a seller to pay a buyer a penalty
if said seller elects to sell said selected item to another
buyer.
68. The system of claim 1, wherein: said buyer's intelligent
negotiation agent transmits its executable code to a remote
location of one of said plurality of sellers' intelligent
negotiation agents to engage in said negotiation with said sellers'
intelligent negotiation agent.
69. The system of claim 1, wherein: said seller's intelligent
negotiation agent transmits its executable code to a remote
location of said buyer's intelligent negotiation agent to engage in
said negotiation with said buyer's intelligent negotiation
agent.
70. The system of claim 1, further comprising: an analytical agent
in communication with said buyer's intelligent negotiation agent
for providing to said buyer's intelligent negotiation agent a
report on market data related to said selected item.
71. The system for automated negotiation for an item procurement of
claim 1, further comprising: a plurality of buyer's
micro-negotiation agents, each said buyer's micronegotiation agent
launched by said buyer's intelligent negotiation agent, each said
buyer's micro-negotiation agent in communication with one of said
at least two of said plurality of seller's intelligent negotiation
agents, such that, when said buyer's intelligent negotiation agent
receives said bid from said at least two of said plurality of
seller's intelligent negotiation agents, said buyer's intelligent
negotiation agent launches said micro-agents.
72. The system for automated negotiation for an item procurement of
claim 71, wherein: each said buyer's micro-negotiation agent is
mobile for self-transmission to a location of one of said seller's
intelligent negotiation agents for engaging therewith in said
negotiation.
73. The system for automated negotiation for an item procurement of
claim 1, further comprising: when said buyer's intelligent
negotiation agent receives said bid from said at least two of said
plurality of sellers' intelligent negotiation agents, said buyer's
intelligent negotiation agent transmits a set of minimally
acceptable buyer's negotiation rules for a negotiation for
procurement of said selected item to said at least two of plurality
of sellers' intelligent negotiation agents, and said at least two
of said plurality of sellers' intelligent negotiation agents
transmits to said buyer's intelligent negotiation agent a set of
seller's minimally acceptable seller's negotiation rules for said
negotiation.
74. The system for automated negotiation for an item procurement of
claim 73, wherein: said buyer's intelligent negotiation agent and
said sellers' intelligent negotiation agents agree to a server
location for engaging in said negotiation.
75. The system for automated negotiation for an item procurement of
claim 73, wherein: said buyer's intelligent negotiation agent and
said sellers' intelligent negotiation agents agree to a programming
language for engaging in said negotiation.
76. The system for automated negotiation for an item procurement of
claim 73, wherein: said buyer's intelligent negotiation agent and
said sellers' intelligent negotiation agents agree to security
protocols for engaging in said negotiation.
77. The system for automated negotiation for an item procurement of
claim 73, wherein: said buyer's intelligent negotiation agent and
said sellers' intelligent negotiation agents agree to encryption
protocols for engaging in said negotiation.
78. The system for automated negotiation for an item procurement of
claim 73, wherein: at least four of said at least two of said
plurality of sellers' intelligent agents transmits to said buyer's
intelligent negotiation agent said a set of seller's minimally
acceptable seller's negotiation rules.
79. The system for automated negotiation for an item procurement of
claim 73, wherein: said buyer's intelligent negotiation agent
eliminates all but two of said plurality of sellers' intelligent
agents for further negotiations.
80. The system for automated negotiation for an item procurement of
claim 73, wherein: said buyer's intelligent agent eliminates all
but one of said plurality of sellers' intelligent negotiation
agents for further negotiations.
81. The system for automated negotiation for an item procurement of
claim 73, wherein: said buyer's intelligent negotiation agent and
one of said plurality of sellers' intelligent negotiation agents
enter into an agreement binding upon a buyer and a seller for
procurement of said selected item.
82. A system for automated closing of procurement transactions
using a computer that communicates over a network, the system
comprising: an intelligent negotiation agent for autonomously
negotiating item specifications for procurement of a selected item
from a list of items, said list of items comprising individual
product items and service items, and an intelligent transaction
agent in communication with said intelligent negotiation agent,
said intelligent transaction agent for autonomously clearing
closing terms to complete a transaction for procurement of said
selected item.
83. The system for automated closing of procurement transactions of
claim 82, further comprising: a plurality of artificial
intelligence program resources, said suite of artificial
intelligence resources accessible by said intelligent analytical
agent, and wherein said analytical agent selects one of said
plurality of artificial intelligence resources for optimal
performance of a pre-identified computation.
84. The system for automated closing of procurement transactions of
claim 83, wherein: said suite of artificial intelligence program
resources comprises genetic programming.
85. The system for automated closing of procurement transactions of
claim 83, wherein: said suite of artificial intelligence program
resources comprises genetic algorithms.
86. The system for automated closing of procurement transactions of
claim 83, wherein: said suite of artificial intelligence program
resources comprises neural networks.
87. The system for automated closing of procurement transaction
systems of claim 86, the system further comprising: at least one
intelligent analytical agent for mining data related to select item
from at least one of a plurality of market data bases, said
analytical agent further for generating a subset of data that most
closely meets a goal, and said analytical agent in communication
with said intelligent transaction agent, wherein when said
intelligent transaction agent receives data from said analytical
agent, said intelligent transaction agent clears said transaction
terms.
88. The system of claim 87, further comprising: said intelligent
negotiation agent for providing transaction support [?]
services.
89. The system of claim 87, wherein: said analytical agent performs
a superscore analysis, said analysis including a weight ranking of
each of a plurality of factors.
90. The system of claim 87, wherein: said analytical agent performs
an economic analysis, said analysis including a weight ranking of
each of a plurality of factors.
91. The system of claim 87, wherein: said analytical agent performs
an accountability index analysis, said analysis including a weight
ranking of each of a plurality of factors.
92. The system of claim 87, wherein: said analytical agent performs
a promotion availability analysis, said analysis including a weight
ranking of each of a plurality of factors.
93. The system of claim 87, wherein: said analytical agent performs
a financial analysis, said analysis including a weight ranking of
each of a plurality of factors.
94. The system of claim 87, wherein: said analytical agent performs
an aggregation availability analysis, said analysis including a
weight ranking of each of a plurality of factors.
95. The system of claim 87, wherein: said analytical agent performs
an insurance availability analysis, said analysis including a
weight ranking of each of a plurality of factors.
96. The system of claim 87, wherein: said analytical agent performs
a financial risk management analysis, said analysis including a
weight ranking of each of a plurality of factors.
97. The system of claim 87, wherein: said analytical agent performs
an arbitrage opportunity analysis, said analysis including a weight
ranking of each of a plurality of factors.
98. The system of claim 87, wherein: said analytical agent performs
a risk management option availability analysis, said analysis
including a weight ranking of each of a plurality of factors.
99. A system for automated closing of procurement transactions
using a computer that communicates over a network, the system
comprising: at least one buyer's intelligent transaction agent, and
at least one seller's intelligent transaction agent in
communication with said at least one buyer's intelligent
transaction agent, said buyer's intelligent transaction agent and
said seller's intelligent transaction agent for coordinating
clearance of transaction terms to close a transaction for a
procurement of a selected product or service item.
100. A system for automated procurement that communicates over a
distributed network, the system comprising: one or more memories
for storing a list of individual product items and individual
service items, an intelligent commercial search agent in
communication with said one or more memories, and one or more
seller showcase databases in communication with said commercial
search agent, said one or more seller showcase databases receiving
market data from at least one of a plurality of market databases,
each said one or more seller showcase databases updated responsive
to a change in said market data, wherein, when one or more of said
seller showcase databases receives from said commercial search
agent a request to receive bids to sell a selected item specified
from said list, said one or more seller showcase databases submits
that bid to said commercial search agent.
101. The system of claim 100, wherein: said one or more seller
showcase databases select one of a plurality of evolutionary
computing resources to evaluate said market data.
102. The system of claim 101, wherein: said one of a plurality of
evolutionary computing resources comprises genetic algorithms.
103. The system of claim 74, wherein: said one of a plurality of
evolutionary computing resources comprises genetic programming.
104. The system of claim 101, wherein: said one of a plurality of
evolutionary computing resources comprises neural networks.
105. The system of claim 100, wherein: said intelligent commercial
search agent selects one of a plurality of evolutionary computing
resources to evaluate each bid from said one or more seller
showcase databases.
106. The system of claim 105, wherein: said one of a plurality of
evolutionary computing resources comprises genetic algorithms.
107. The system of claim 105, wherein: said one of a plurality of
evolutionary computing resources comprises genetic programming.
108. The system of claim 105, wherein: said one of a plurality of
evolutionary computing resources comprises neural networks.
109. The system of claim 105, wherein: said intelligent commercial
search agent selects two of said bids for a negotiation.
110. A system for automated negotiation for offering an item for
procurement using a computer that communicates over a distributed
network, the system comprising: a sellers' intelligent negotiation
agent for sending and receiving information regarding at least one
selected item to and from a plurality of buyer's intelligent
negotiation agents, said selected item being one of a group of
individual product items and individual service items, and said
plurality of buyer's intelligent negotiation agents for sending and
receiving information regarding said selected item to and from said
sellers' intelligent negotiation agents, wherein, when said
sellers' intelligent negotiation agent sends a request for bids to
buy said selected item to said plurality of buyer's intelligent
negotiation agents, said plurality of buyer's intelligent
negotiation agents engages in a negotiation with said sellers'
intelligent negotiation agent for procurement of said selected
item.
111. The for automated negotiation for offering an item for
procurement of claim 110, wherein: said sellers' intelligent
negotiation agent accepts a bid from one of said buyer's
intelligent negotiation agent to buy said selected item.
112. A method for automated negotiation for procurement of an item
using computers that communicate over a distributed network, the
method comprising: sending to a buyer's intelligent negotiation
agent a response to a buyer's initial query regarding a selected
item, said selected item being one of a group of individual product
items and individual service items, receiving said response from at
least one of a plurality of said seller's intelligent negotiation
agents, and engaging in a negotiation between said buyer's
intelligent negotiation agent and each of said plurality of
seller's intelligent negotiation agents for procurement of said
selected item.
113. The method for automated negotiation for procurement of an
item of claim 112, wherein: said buyer's initial query comprises a
request for bids to sell said selected item.
114. The method of claim 112 wherein: said response comprises a bid
to sell said selected item, said bid having a set of seller's
specifications, and said negotiation further comprises comparing
each of said seller's specifications against a set of buyer's
specifications, selecting an optimal new set of buyer's
specifications in view of a preprogrammed buyer strategy, and
transmitting said new set of buyer's specifications to at least one
of said sellers' intelligent negotiation agents.
115. The method for automated negotiation for procurement of an
item of claim 112, wherein: said initial query comprises a set of
buyer's specifications for said selected item, said response to
said query comprises a set of seller's specifications for said
selected item, said negotiation further comprises developing a new
set of buyer specifications responsive to said seller's
specifications, and transmitting said new set of buyer's
specifications to said one of said sellers' intelligent negotiation
agents.
116. The method of claim 15, wherein: said negotiation further
comprises comparing said new set of buyer's specifications against
a set of seller's specifications, selecting an optimal set of new
seller's specifications in view of a preprogrammed seller strategy,
and transmitting said new set of seller's specifications to said
buyer's intelligent negotiation agent.
117. The method of claim 115, further comprising: receiving a
constantly changing stream of information from a plurality of
market databases, and said buyer's intelligent negotiation agent
seeking an optimal match for each specification of said set of
buyer's specifications in said bids from said sellers' intelligent
negotiation agents.
118. The method of claim 112, further comprising: transmitting a
buyer's intelligent negotiation agent's acceptance of the bid from
one of said sellers' intelligent negotiation agents to that one
seller's intelligent negotiation agent.
119. The method of claim 118, further comprising: providing an
intelligent transaction agent in communication with said buyer's
intelligent negotiation agent, transmitting transaction terms to
said intelligent transaction agent, and autonomously clearing said
transaction terms to complete a transaction for procurement of said
selected item responsive to said acceptance of said bid.
120. The method of claim 112, further comprising: transmitting a
counter-offer from said buyer's intelligent negotiation agent to at
least one of said sellers' intelligent negotiation agents said
counter-offer responsive to said bid.
121. The method of claim 112, further comprising: receiving a bid
from at least eight of said sellers' intelligent negotiation
agents, and selecting only four of said sellers' intelligent
negotiation agents for buyer's intelligent negotiation agent to
continue said negotiation.
122. The method of claim 112, further comprising: receiving a bid
from at least four of said sellers' intelligent negotiation agents,
and selecting only two of said sellers' intelligent negotiation
agents for buyer's intelligent negotiation agent to continue said
negotiation.
123. The method of claim 122, further comprising: selecting one of
said two of said sellers' intelligent negotiation agents for said
buyer's intelligent negotiation agent to continue said
negotiation.
124. The method of claim 112, further comprising: said buyer's
intelligent negotiation agent seeking an optimal match from said
seller's intelligent negotiation agents for each specification of a
set of buyer's item specifications, and said seller's intelligent
negotiation agents each seeking an optimal match from said buyer's
intelligent negotiation agent for each specification a set of
seller's item specifications.
125. The method of claim 112, further comprising: receiving a
constantly changing stream of information from a plurality of
market databases, and seeking an optimal match for each
specification of a preprogrammed set of buyer's specifications in
said bids from said sellers' intelligent negotiation agents.
126. The method of claim 125, further comprising: selecting one of
a plurality of evolutionary computation resources to seek said
optimal match.
127. The method of claim 112, further comprising: receiving a
constantly changing stream of information from a plurality of
market databases, seeking an optimal match from said buyer's
intelligent negotiation agents for each specification of a
preprogrammed set of seller's specifications.
128. The method of claim 127, further comprising: selecting one of
a plurality of evolutionary computation resources to seek said
optimal match.
129. The method of claim 112, further comprising: transmitting a
programming code of said buyer's intelligent negotiation agent to a
remote location of one of said plurality of seller's intelligent
negotiation agents for further engaging in said negotiation at said
location with said seller's intelligent negotiation agent.
130. The method of claim 112, further comprising: receiving from an
analytical agent a report on market data related to said selected
item.
131. The method of claim 112, further comprising: receiving said
bid from at least two of said plurality of sellers' intelligent
negotiation agents, transmitting to said seller's intelligent
negotiation agents a set of minimally acceptable buyer's
negotiation rules for a negotiation between said seller's
intelligent negotiation agents and said buyer's intelligent
negotiation agent for procurement of said selected item, and
receiving from each of at least two of said plurality of sellers'
intelligent negotiation agents a set of seller's minimally
acceptable seller's negotiation rules for said negotiation.
132. The method of claim 131, further comprising: entering into an
agreement between said buyer's intelligent negotiation agent and
one of said plurality of said seller's intelligent negotiation
agents binding upon a buyer and a seller for procurement of said
selected item.
133. A method for automated closing of procurement transactions
using computers that communicate over a network, the method
comprising: negotiating item specifications autonomously for
procurement of a selected item, said selected item one of a group
of individual product items and individual service items, and
autonomously clearing closing terms to complete a transaction for
procurement of said selected item.
134. The method of claim 133, further comprising: selecting one of
a plurality of evolutionary computation resources for said clearing
closing terms.
135. The method of claim 133, further comprising: instructing an
intelligent analytical agent to mine data related to said selected
item from at least one of a plurality of market data bases,
autonomously generating a subset of data that best satisfies a
preprogrammed goal, transmitting said subset of data to an
intelligent transaction agent, and intelligent transaction agent
autonomously clearing said transaction terms.
136. A method for automated procurement of an item using computers
that communicate over a distributed network, the method comprising:
storing a list of individual product items and individual service
items, receiving market data from a plurality of market data bases,
transmitting said market data to a remote node in a distributed
network, updating said showcase databases responsive to a change in
said market data, receiving from an intelligent commercial search
agent a request to receive bids to sell a selected specified from
said list, and submitting that bid to commercial search agent.
137. A system for automated arbitrage using computers that
communicate over a distributed network, the system comprising: a
plurality of sellers' intelligent negotiation agents, at least one
buyer's intelligent negotiation agents, a dynamic intelligent
negotiation agent in communication with said plurality of sellers'
intelligent negotiation agents and in communication with said at
least one buyer's intelligent negotiation agent, said dynamic
negotiation agent having a buyer mode and a seller mode, each said
sellers' intelligent negotiation agent having an authority from an
associated seller to sell at least one item needed to fulfill a
procurement interest of said buyer's intelligent negotiation agent,
wherein, when each of said sellers' intelligent negotiation agents
receives a request from said dynamic intelligent negotiation agent
in said buyer mode for a bid to sell one of said items, each of
said sellers' intelligent negotiation agent submits that bid to
said dynamic intelligent negotiation agent, and wherein, when said
dynamic intelligent negotiation agent in said buyer mode determines
to accept said bid from said sellers' intelligent negotiation
agent, said dynamic agent in said seller mode submits a bid to sell
that item to said buyer's intelligent negotiation agent.
138. The system of claim 137, wherein: said buyer's intelligent
negotiation agent submits an acceptance of said bid to sell that
item to said dynamic intelligent negotiation agent.
139. The system of claim 138, wherein: said dynamic intelligent
negotiation agent accepts said bid to sell one of said items from
said sellers' intelligent negotiation agent.
140. The system of claim 137, further comprising: a buyer
associated with said buyer's intelligent negotiation agent, said
sellers' intelligent negotiation agent instructs said associated
seller to ship said item directly to said buyer.
141. The system of claim 137, wherein: said dynamic intelligent
negotiation agent transmits its executable code to a remote
location of at least one of said sellers' intelligent negotiation
agents and said buyer's intelligent negotiation agent for
mobility.
142. The system of claim 137, wherein: said bid to said dynamic
intelligent negotiation agent from said sellers' intelligent
negotiation agent includes at least one contract contingency
authorizing said seller to pay said dynamic intelligent negotiation
agent a penalty if said seller elects to sell said selected item to
another than said dynamic intelligent negotiation agent.
143. The system of claim 142, wherein: said bid to said buyer's
intelligent negotiation agent by said dynamic intelligent
negotiation agent includes at least one contract contingency
authorizing said dynamic intelligent negotiation agent to pay said
buyer's intelligent negotiation agent a penalty if said seller
elects to sell said selected item to another than said dynamic
intelligent negotiation agent.
144. The system of claim 137, wherein: said dynamic intelligent
negotiation agent selects one of a plurality of evolutionary
computation resources to send and receive messages to and from said
plurality of seller's intelligent negotiation agents and said at
least one buyer's intelligent negotiation agents.
145. The system of claim 144, wherein: said plurality of
evolutionary computation resources comprises genetic
algorithms.
146. The system of claim 144, wherein: said plurality of
evolutionary computation resources comprises genetic
programming.
147. The system of claim 144, wherein: said plurality of
evolutionary computation resources comprises neural networks.
148. The system of claim 144, wherein: said dynamic intelligent
negotiation agent in said buyer's mode selects one of said
plurality of evolutionary computation resources for optimal
computation of a match in said bids from said sellers' intelligent
negotiation agents for each specification of a set of buyer's
specifications for said at least one of a plurality of bundles.
149. A method for automated arbitrage using computers that
communicate over a distributed network, the method comprising:
transmitting to each of a plurality of sellers' intelligent
negotiation agents a request for a bid to sell at least one item
needed to fulfill a procurement interest of a buyer's intelligent
negotiation agent, receiving from said seller's intelligent
negotiation agent that bid, and transmitting to said buyer's
intelligent negotiation agent a bid to sell that item.
150. The method of claim 149, further comprising: receiving from
said buyer's intelligent negotiation agent an acceptance of said
bid to sell that item.
151. The method of claim 150, further comprising: accepting said
bid from said seller's intelligent negotiation agent to sell said
at least one item.
152. The method of claim 151, further comprising: shipping said
item directly from a seller associated with said seller's
intelligent negotiation agent to a buyer associated with said
buyer's intelligent negotiation agent.
153. The method of claim 149, wherein: said bid from said sellers'
intelligent negotiation agent includes at least one contract
contingency authorizing a seller associated with said seller's
intelligent negotiation agent to pay a penalty if said seller
elects to sell said selected item to another buyer.
154. The method of claim 153, wherein: said bid to said buyer's
intelligent negotiation agent includes at least one contract
contingency authorizing payment of a penalty to said buyer's
intelligent negotiation agent if said seller elects to sell said
selected item to another buyer.
155. The method of claim 149, further comprising: selecting one of
a plurality of evolutionary computation resources to send and
receive messages to and from said plurality of seller's intelligent
negotiation agents and said at least one buyer's intelligent
negotiation agents.
156. The system of claim 137, wherein: a seller associated with
said seller's intelligent negotiation agent ships said item
directly to a buyer associated with said buyer's intelligent
negotiation agent.
157. A computer program product comprising a machine readable
medium on which is provided program instructions for performing a
method for procurement of an item using computers that communicate
over a distributed network, the program instructions comprising:
program code for sending to a buyer's intelligent negotiation agent
a response to a buyer's initial query regarding a selected item,
program code for said selected item being one of a group of
individual product items and individual service items, program code
for receiving said response from at least one of a plurality of
said seller's intelligent negotiation agents, and program code for
engaging in a negotiation between said buyer's intelligent
negotiation agent and each of said plurality of seller's
intelligent negotiation agents for procurement of said selected
item.
158. A computer program product comprising a machine readable
medium on which is provided program instructions for performing a
method for automated arbitrage using computers that communicate
over a distributed network, the program instructions comprising:
program code for transmitting to each of a plurality of sellers'
intelligent negotiation agents a request for a bid to sell at least
one item needed to fulfill a procurement interest of a buyer's
intelligent negotiation agent, program code for receiving from said
seller's intelligent negotiation agent that bid, and program code
for transmitting to said buyer's intelligent negotiation agent a
bid to sell that item.
Description
SUMMARY OF THE INVENTION
[0001] This application claims the benefit of U.S. Provisional
application No. 60/162,932, filed Nov. 1, 1999, U.S. application
Ser. No. 091705,177, filed Nov. 1, 2000, U.S. application Ser. No.
09/704,342, filed Nov. 1, 2000, U.S. application Ser. No.
09/704450, filed Nov. 1, 2000, U.S. application Ser. No. 09/704361,
filed Nov. 1, 2000, U.S. application Ser. No. 09/705178, filed Nov.
1, 2000, and U.S. Provisional application No. 60/250,819, filed
Dec. 1, 2000.
FIELD OF THE INVENTION
[0002] The invention relates to an integrated automated commercial
system and the apparatus and methods thereof. In a distributed
computer network environment, novel databases, data search
approaches, data mining, data analysis and data synthesis methods
are used to provide a system for conducting disintermediated,
point-to-point electronic commerce. Rivers of data are continuously
automatically analyzed, and data is selected for inclusion in
marketing promotion-driven showcase databases. Buyer driven search
queries initiate the commercial negotiation process by which
multiple sellers simultaneously compete for orders in a
multivariate way (beyond price alone). Prospective buyers and
sellers may collaborate prior to an initial search petition.
[0003] The system uses intelligent mobile software agents to
analyze, search, negotiate for and complete commercial
transactions. To retain independence, the agents are endowed with
complex adaptive artificial intelligence capabilities. In one
embodiment, Intelligent Negotiation Agents (INAs) cooperate or
modify so as to provide commercial sales aggregation or arbitrage
capabilities. Custom search and custom production capabilities are
also implemented, thereby allowing increasingly efficient
made-to-order services.
[0004] The interaction of specific-function agents in the
multi-agent system (MAS) operates within the distributed database
system, one embodiment of which includes vertical industry
cooperatives [cooperative communications networks] (CCNs). The
interaction of, among other things, analytical agents (AAs) with
INAs and intelligent transaction agents (ITAs) and of INAs with
ITAs creates a complex commercial system that emulates
self-organizing commercial relationships with enhanced
efficiencies.
[0005] The cluster of inventions comprising the present system
represents the consolidation of solutions to large economic systems
integration problems.
BACKGROUND OF THE PRESENT INVENTION
[0006] The emergence of the Internet has caused a shift in the
methods of commercial activity towards automated purchasing,
marketing, sales and distribution of products, services and
bundles. The automated aspects of electronic commerce allow a
one-to-one relationship between seller and buyer as compared with
the traditional mass production and sales approach. However, most
electronic commerce sales systems resemble simple catalog sales or
intermediated exchange: Neither of these main approaches satisfies
the ideal of automated commerce.
[0007] What is needed is a purchasing, sales, marketing and
production system that emulates the way customers actually buy and
manufacturers produce goods and services. By mirroring the economic
psychology of buyers, a system can be developed that (1) is
demand-based, (2) sets up a seller-side competition for buyers, (3)
uses multivariate negotiation processes, (4) uses interactivity,
(5) is information rich, (6) exploits systemic adaptivity that
learns from data analysis and synthesis, (7) facilitates buyer
aggregation and (8) employs customization. Applicant is the
inventor of a system (Solomon, PCT WO 01/33464 A1), which performs
these functions primarily in a centralized way (i.e., using
intermediation processes, such as exchange or auction), but no
system does so in a disintermediated way. The challenge of
automated commercial operations is to develop such a
disintermediated electronic commerce system. The present invention
addresses these problems in novel and non-obvious ways.
[0008] The present invention derives from the convergence of
several technologies involving distributed computing systems and
multi-agent systems.
[0009] The evolution of the Internet, particularly the World Wide
Web (Web), emerged as a distributed computing medium in which
independent computers can access information by using browser or
e-mail communications software. But the main uses of the Internet
in e-commerce have focused on intermediated transactions. For
consumers, most transactions resemble an electronic catalogue sales
system, while for businesses most electronic transactions occur at
a centralized portal or exchange.
[0010] However, a new generation of distributed database
architectures is emerging with promising commercial applications.
One prominent example of a new decentralized database structure
that is organized for disintermediated information exchange is the
GRID. Originally patterned after the electric power grid, which can
move electricity from point to point, the GRID is intended to use
distributed database architectures for large bandwidth applications
such as supercomputer data flows.
[0011] These new distributed database architectures allow new data
search and analytical methods. Traditionally, search engines have
accessed large central databases that accumulate and structure the
collection of data over a period of time. These technologies are
limited to relational database structures, and restricted in
analytical complexity. The new search technologies overcome these
problems by exploiting distributed computing architectures and
object-relational data structures.
[0012] Traditional data mining techniques have employed pattern
recognition and statistical modeling algorithms in order to
organize and assess large pools of data. One outcome of the use of
data mining has been in the area of collaborative filtering where
recommendations are made to customers on the basis of inferences of
other customers' similar interests. But a new generation of
artificial intelligence technologies provides the ability to
produce complex data analyses and syntheses that reveal more
accurate predictions because they adapt to changing circumstances.
By integrating data analysis and synthesis tools with search and
transaction tools, these recommendation and predictive capabilities
are more useful.
[0013] Businesses have for decades tried to automate their internal
computer and communications systems in order to improve efficiency
and promote competitive advantages. One of the first attempts at
business automation involved the use of "electronic data
interchange" (EDI). EDI was a precursor to electronic commerce
because it set up a system for businesses to communicate
electronically in order to complete and track financial
transactions. Most of the transactions used by EDI systems are
financial, dealing primarily with payment processing. EDI simply
automates paper processing of payment notices, remittances and
receipt records, directly between companies.
[0014] A technology that is emerging to succeed EDI involves a new
programming language--XML--and business registry--UDDI. These new
technologies allow a more robust communication between businesses
because products, services and bundles are indexed and catalogued
for direct access. Though more robust than EDI, XMUUDDI systems are
merely passive information-based formats that link businesses,
similar to the yellow-pages.
[0015] Technologies to connect machines and people have been
advanced by the advent of graphic user interface (GUI) technology
applied to PCs through advanced operating systems. In addition to
simpler GUIs, translation software has been used to bridge the gaps
between different software applications. The development of a new
generation of inter-agents that interface with human users and
computer programs is a key evolution towards simpler, yet more
powerful commercial transactions.
[0016] Multi-Agent Systems (MASs) are not new in academic circles.
The attempt to develop MASs in distributed computing environments
has been active for over a decade. With the increased automation of
business computing systems, MASs have reshaped the factory floor,
securities trading systems and complex communications networks.
With the advent of AI technologies, most prominently GP, GA, NN and
FL, MASs have emerged as a reborn technology category for computer
scientists.
[0017] One of the most practical uses for MASs applies to
negotiation systems. The development of the "contract net" (k-net)
system for distributed problem solving acts as the pioneer idea for
mediating distributed computer encounters, particularly for
efficiently dividing limited computation resources in a network.
Building on the k-net platform, prominent based market models for
transaction negotiation include the Fishmarket, the Michigan
AuctionBot, Tete-a-tete and SWARM. These systems attempt in
different ways to model contracting processes so as to facilitate
commercial transactions.
[0018] In addition to these transaction models, a subset of the
computer science academic literature describes coordination of
agents in a MAS. Organizing cooperating intelligent agents is a key
challenge of computer science, because it involves calibrating
rules that provide neutrality to the coordination (typically of
buyers) in complex self-organizing systems.
[0019] Though there are automated systems that emulate manual
transaction processes, most approaches merely represent
evolutionary progress in the field. E-commerce approaches seek to
integrate post-sale systems with payment processing and CRM
systems. This is necessary to complete and track transactions and
to develop and enhance personalized customer relationships in a
single centralized system. Even more advanced automated systems can
more fully integrate complex marketing and financial processes into
the transaction system. Further, the transaction system can be part
of a unified system that includes data analysis and synthesis and
negotiation processes.
SUMMARY OF THE MAIN EMBODIMENTS OF THE INVENTION
[0020] The present invention consists of two interdependent
systems: (I) A network operating system for databases, database
search, data analysis and synthesis, database inter-agents and data
collaboration, and (II) A multi-agent system for negotiation and
completion of transactions between parties.
[0021] The first system consists of Cooperative Communications
Networks (CCNs) that are comprised of (primarily vertical) industry
participants. Participants use database showcases to stream data on
products, services and bundles, continuously in real time. Showcase
databases use showcase inter-agents to automate the item selection
process; such inter-agents access analyses of market trends and
behavior to make item selections for inclusion into a showcase. In
additional embodiments, CCNs may be horizontal or customized: Such
configurations can be buyer biased, such as a very large
corporation automatically sourcing vendor orders.
[0022] Showcases are accessed by commercial search agents (CSAs).
Because the showcases of each vertical industry are continuously
updated, the search process is both fast and accurate. The CSA uses
information obtained from data mining processes to focus the search
request. The CSA acts as an initial commercial search query in most
cases. Further negotiation processes follow the initial search
after ranking search results according to buyer priorities. CSAs
can make requests based on numerous variables beyond price
alone.
[0023] Detailed information on customer and seller accountability
in addition to promotions, such as time-sensitive offers, and risk
management options (RMOs) are provided at the showcase and CSA
levels for more informed and accurate searches and for maximized
commercial opportunities.
[0024] In order to establish a system to acquire customized items
that are not included in showcases, a collaboration process occurs
between buyer inter-agents (B-IAs) and seller inter-agents (S-IAs).
This process integrates with a made-to-order (MTO) sourcing system
in which item specifications are indicated by a B-IA to at least
two S-IAs. After being informed by their respective AAs, the IAs
provide specific data pertaining to the buyer item specification
request. By allowing at least two competing sellers to provide item
specifications on substitutable competitive items that satisfy
minimum buyer standards, a comparable item competition can occur.
An interaction between a B-IA and S-IAs can occur in order to
clarify the item specifications prior to the bidding process. Once
sellers respond with items that satisfy buyer specifications, the
process proceeds to the CSA for the commencement of the initial
pricing and bidding processes. The MTO collaboration process
effectively bypasses the showcase database system, but integrates
with the MAS.
[0025] Analytical agents (AAs) are employed at the database system
level for data mining, data analysis and data synthesis. AAs get
continuous data inputs of general economic and market trends as
well as company and product/service information. AAs have several
functions, including making recommendations by using advanced
collaborative filtering techniques. In addition, AAs synthesize
information in the form of producing customized reports.
Furthermore, AAs access services such as credit and accountability
indices, finance and insurance opportunities, RMOs, promotions and
computational resources. Such information breadth makes AAs an
integral data computation resource for other agents in the system,
most prominently ITAs and INAs.
[0026] AAs use Evolutionary Computation (EC) technologies in order
to develop economic scenario forecasts. To do this, genetic
programming (GP) approaches are used, as well as genetic algorithms
(GA) and neural network (NN) methods, that compare the constantly
changing market conditions with customer preferences and provide
adaptive real time analysis and customized advice.
[0027] Because they are organized in vertical industry cooperative
communities, cooperative communications networks (CCNs) are
maintained by participating sellers. CSAs are free for basic
services but can access AA services. AAs have various levels of
services that are accessible by users for supplemental fees.
[0028] In order to conduct searches and to perform negotiations and
transactions, the system uses codes to transfer information. These
codes may be processed using languages such as the extensible
mark-up language (XML) and registries (UDDI, RDF) as well as
proprietary information exchange methods (SOAP). Some of the mobile
program codes are written in the Java, Java 2, Java Beans, Jini,
C++, C# and other languages.
[0029] Inter-agents are used to perform functions between human and
machine. For instance, showcase (or seller) inter-agents (S-IAs)
automate the continuous updating of showcase databases. Buyer
inter-agents (B-IAs) are also used to interface between users and
their CSAs and INAs.
[0030] The multi-agent system (MAS) is the core system and process
for the negotiation and completion of transactions. The MAS
consists of intelligent negotiation agents (INAS) and intelligent
transaction agents (ITAs). INAs have buyer (b-INA) or seller
(s-INA) roles; similarly, ITAs have b-ITA and s-ITA roles.
[0031] Once a CSA has initiated a search query for information (and
promotions) to CCN showcase databases, at least two s-INAs respond
with an initial ask price, as well as alternative prices for
different product or service features, quality, quantity, delivery
times, etc. The buyer may request bidding information about bundles
of products and services as well as individual items. After a
pre-negotiation session that sets the terms for the negotiation
sessions, the multivariate negotiation process commences once a
b-INA is launched to interact with the s-INAs. The multilateral
negotiation process occurs when the b-INA negotiates with multiple
vendor s-INAs simultaneously. Such INA interactions may occur in
parallel at buyer or seller locations. Multiple sellers are
eventually limited to two sellers per session until ultimately one
is selected by the buyer.
[0032] Because b-INAs initially negotiate simultaneously with at
least two s-INAs, and because the INAs are mobile (and cannot be in
two places simultaneously), in order to overcome latency lags
b-INAs launch micro-agents that complete simultaneously interactive
negotiations with multiple s-INAs
[0033] INAs use negotiation, auction and pricing strategy modules
to establish, modify, evaluate and respond to bids. Further,
specific approaches are used to conceal negotiation strategies,
particularly time-based methods. Additionally, INAs can employ
various "personalities" on a spectrum of attitudes in order to
accelerate or decelerate the negotiations.
[0034] INAs are informed by AAs, which provide data analysis and
synthesis functions, such as collaborative filtering-based
recommendations, scenario forecasts and trend histories, that are
crucial for effective negotiations.
[0035] INAs themselves employ artificial intelligence (AI)
technologies. These autonomous agents use evolutionary computation
methods in which computer programs learn and adapt to the changing
commercial environment. The main evolutionary computation
approaches include GPs, GAs and NNs among others. Because they are
evolutionary, they use principles of "natural selection" in which
they conduct runs of untested programs against successful known
computer programs and criteria for program improvement. Such
evolutionary programs constantly adapt within the constraints of
time and computation resources. Evolutionary computation can be
layered so as to maximize computer resource efficiencies in such a
way that simpler tasks require minimum computation resources and
maintain maximum mobility, while complex tasks employ increased
computation resources.
[0036] The use of artificial intelligence by INAs produces
autonomous agents and self-organizing commercial systems. To
provide an analogy of INA operation with AI, the multi-agent system
resembles a road system with various autonomous cars operating
simultaneously. The AI uses recognized rules for cars to interact,
yet provides enough independence between each autonomous vehicle
that all functions are not pre-destined. Each car has its own
endowments of power and efficiency as well as starting and
destination points in space and time. Each, however, operates both
within the limits of varying roadways and road conditions. The
overall system operates according to rules that allow an optimized
flow of mobile activity. Yet, because agents have complex and
changing priorities, they have varying associations. Taken
together, the agents create a dynamic system that adapts as
conditions and priorities change.
[0037] Negotiation agents operate in a computer system by sending
program code and data between machines to fulfill a goal of
completing a transaction. However, there is an additional layer in
which the INAs are mobile. In this embodiment, the negotiation
agents themselves move between machines. Negotiations can occur
between agents at specific locations, at multiple locations or
between alternating locations. INA mobility involves replicating
program code, satisfying security protocols, pruning program code
for increased mobility, retrieving layers of AI computation
resources when needed, integrating essential database functions and
accessing updated programming instructions from a home port.
Mobility has numerous advantages for participants, including
efficiency enhancements and operation in a system with
communication constraints.
[0038] Cooperative INAs (c-INAs) are comprised of groups of buyer
INAs that band together in various ways in order to negotiate
optimal deals. There are three types of c-INA applications: (1)
neutral brokers used for intermediation, (2) aggregation of buyers
and (3) multi-item bundles. There are various complex ways of using
C-INAs for buying and selling combinations of items. In one of
these functions, C-INAs can be used for the aggregation of buyers
for the acquisition of multi-item bundles that can be customized so
that specific items in each customer package are individually
tailored. C-INAs allow the disintermediation of a wholesale layer
in the distribution and production system by streamlining the sales
process and by also providing discount buying power.
[0039] Like INAs in general, c-INAs are typically either buyer- or
seller-biased. B-C-INAs emphasize aggregation operations. On the
other hand, S-C-INAs are used as sellers must provisionally
cooperate in order to calculate buyer values, particularly for the
purpose of selling combination item bundles.
[0040] Pre-established multi-item bundles, such as a pre-configured
combination of computer hardware and software, can be treated as a
single item for the purposes of this system. On the other hand,
open bundles consisting of multiple items, require the selection of
the buyer to assemble, and involve much more complex negotiations.
Historically, multi-item bundle bidding has emphasized the sale of
multiple items from one seller to multiple buyers (such as an FCC
spectrum auction). Despite the difficulty of complex calculations
to select buyer bidders, the present system accommodates both a
single buyer bidder with multiple sellers as well as multiple
buyers (during and after aggregation) with a single or multiple
sellers. By cooperating, multiple sellers (using c-INAs) can behave
as a single seller strictly for the purposes of calculating buyer
bids, and thus determining the appropriate multi-item bundle buyer
winner(s). The present system applies combinatorial auction
processes to a unique commercial implementation of a multi-agent
system.
[0041] Dynamic INAs (d-INAs) are double agents that switch roles
from buyer to seller and vice-versa. D-INAs are used for arbitrage
functions in which products, services and bundles are bought and
sold at different locations for an immediate profit. In these
instances, information currency is critical, so AAs are
particularly important.
[0042] INAs interact with intelligent transaction agents in order
to obtain information necessary to complete transactions. ITAs
interact with AAs in order to analyze and synthesize both general
economic data and specific buyer/seller information. Once ITAs
clear a transaction, for example, with a credit check or financing
approval, the negotiation can be completed.
[0043] The interaction of specific time-sensitive functions occur
in sequential order with the use of different appliances until the
teams goal is completed; multiple functions may be processed
simultaneously, with different orders at different times, so varied
orders of completion will occur. In general, while maximum
temporary efficiencies do exist, there is not necessarily a single
way to prepare all of the projects to satisfy orders via the
processing of specific operation sequences.
Advantages of the Present System
[0044] There are numerous advantages of the present system over
earlier technologies. These advantages involve (1) distributed
database architectures, (2) database search methods, (3) automated
collaboration methods for electronic sourcing, (4) evolutionary
computation-based data analysis and synthesis applications, (5) the
use of AI in negotiation systems, (6) marketing and financial
services network integration, (7) multivariate and multilateral
interactive negotiation processes in a distributed network
environment, (8) item customization, (9) mobility processes of
INAs, (10) complex negotiation and auction approaches, (11) bidding
for products, services and bundles using dynamic pricing approaches
and, finally, (12) aggregation and arbitrage capabilities in a
distributed network. Taken together, these system and method
advantages confer sustainable competitive advantages for commercial
participants by enhancing efficiencies and productivity and by
optimizing costs.
[0045] The distinctive use of showcase databases in vertical
industries automates processes in which rivers of data are
continuously analyzed and selected. The search agent (CSA) is fast
and accurate as it assesses the distributed network in each
vertical CCN because each showcase is constantly replenished and
updated. Consequently, the system adapts rapidly because prices
change continuously based on market factors. Such a system is
especially well suited for revenue management in which prices are
dynamic for high peak and low peak times. In addition, since the
distributed system adapts to changing prices, the system
architecture has self-organizing aspects similar to trading
bazaars. Finally, the system architecture is designed to integrate
into supply chain management (SCM), enterprise resource planning
(ERP) and customer relationship management (CRM) software
systems.
[0046] CSAs also integrate with promotions and risk management
options (RMOs) to invite customers with incentives such as
time-sensitive promotional opportunities. This marketing
integration mirrors how commercial systems actually work, but is
missing in prior systems. For example, products may be bundled with
services (financing, warranties, insurance, etc.), product features
may be upgraded, or delivery time accelerated, in order to benefit
unique buyer preferences. Further, proximity marketing is
integrated with mobility in a MAS by providing time sensitive
promotional opportunities to agents at a particular place. This
advantage provides a bias to promoters that can use greater
computation resources at their preferred location in order to
maintain competitive advantages in negotiations.
[0047] Analytical agents (AAs) go beyond the typical pattern
recognition and data mining tools. By using new generation
evolutionary computation (EC) technologies, AAs are powerful AI
applications that inform and integrate with CSAs, IAs, INAs and
ITAs. AAs process complex data analyses and syntheses to increase
system efficiencies. AAs are the eyes of the system, while GP is
the brain. Because they use AI and evolutionary computing
processes, the system actually "thinks." Consequently, AAs can
anticipate market changes based on scenario forecasts. The ability
of AAs to adapt their programming to accommodate changing market
situations is a critical step forward in research capabilities.
This goes far beyond limit-order type securities program trading
that previous computer exchange technologies have employed.
[0048] Inter-agents intermediate between agents, on the one hand,
and, on the other, between human and machine in novel ways. These
unique applications provide the advantages of system integration
and modularity.
[0049] The negotiation-enabled MAS is intended to produce a
computational system that mirrors the complex commercial psychology
of markets. In essence, the system develops a process that emulates
intuitive methods for commercial procurement. Thus, businesses and
consumers can conduct commercial activities the way they prefer,
namely, by employing direct contact approaches. For example,
specific users employ regular patterns of commercial behavior.
Hence, project-driven transactions can evolve into long-term
business relationships. The automation and efficiency aspects of
the commercial negotiation and transaction aspects of the MAS
increase value in the supply chain.
[0050] INAs provide disintermediated and automated negotiation in a
distributed environment that emulates ordinary commercial
relationships. Further, the INAs use multivariate negotiation
beyond merely price alone leading to a far more robust negotiating
environment. INAs promote competition between sellers, thus
enhancing market efficiencies for buyers, by using simultaneous
multilateral negotiation techniques. INAs go beyond earlier systems
not only because of their integration with AAs, CSAs and ITAs but
because they use AI applications. By using EC technologies--such as
GP, GA and NN as well as integrated negotiation, auction and
pricing strategy modules--INAs behave more independently than
earlier systems. Negotiation session parameter selection is
enhanced by intelligent agents endowed with "judgment" for
promoting optimal commercial trading processes. Such autonomy is
particularly suited to the dynamism of the distributed database
system.
[0051] C-INAs further empower customers by allowing both
aggregation and complex multi-item sales. Computational systems
that use aggregation resemble multiplayer commercial markets
because such processes use global information to benefit both
buyers and sellers. In addition, d-INAs allow intermediary-free
arbitrage that facilitates complex shifting trading role-playing.
Such applications represent a dramatic leap beyond current
intermediated business-to-business exchanges.
[0052] Marketing and financial services are integrated into the
system in novel ways. Marketing opportunities are integrated with
showcases, AAs, CSAs, INAs and ITAs by accessing constantly updated
promotion modules. Marketing services include promotions, proximity
and wireless marketing opportunities, RMOs, transaction
contingencies and time-sensitive offers. Financial services include
accountability and credit reporting, banking offers and insurance,
warranty and other risk analysis and risk limiting opportunities.
Finally, AI requires robust computation resources, which are
provided as layered services. By cross-selling these dozens of
specific services continuously in the distributed network
environment, the system is flexible, scalable, pragmatic,
integrative, self-organizing and effective. These services are sold
in layers as needed by customers.
[0053] Lastly, agents operate in a distributed MAS with autonomy
and mobility because they apply AI methods in a demand-initiated
negotiation process. Mobility has several advantages in the present
system, including (1) communications failsafe in the event of
interruption, (2) less cost because of enhanced efficiency, (3)
reducing lags in negotiation by eliminating communication latency
and (4) providing the neutrality of a level playing field between
buyers and sellers in order to overcome bias.
Implications of the Present Invention
[0054] In general, by providing information and analytical tools,
the system provides both buyers and sellers with a shorter learning
curve in making and processing transactions, as well as greater
diversity of choice. The system thereby promotes increasingly fair
and efficient transactions. Since the systems database architecture
is a "co-op," it is maximally neutral and transparent to both
buyers and sellers.
[0055] For sellers, the system provides increased market reach,
increased efficiency and, consequently, tighter production cycles
that contribute to reduced inventory. In addition, the system
streamlines the sellers own acquisitions and thereby reduces supply
lags. As a consequence of these efficiencies, transactions are
increasingly project-based, and supplier relations are increasingly
flexible. The whole supply chain functions more efficiently. These
efficiencies not only limit response times, but smooth out supply
and demand imbalances, including lags that develop from reduced
information which tend to cause increased market friction. The
system allows companies to minimize inventory by pre-selling items
before making them.
[0056] The present system causes little disruption to existing
commercial systems because it emulates them in the computational
sphere. The system integrates well with current company ERP, CRM
and SCM software systems. By automating such information exchange,
negotiation, marketing and transaction processes, productivity
rates are increased. Taken together, these advantages imply a
sustainable competitive advantage for commercial sellers.
[0057] For buyers, increased information afforded by the system
provides maximum value. The system creates, promotes and enhances
competition among sellers, making markets increasingly efficient
for buyers. Buyer choices are increased and transactions costs
diminished. Multilateral competition for a buyer in a distributed
computational environment increases buyer efficiency and
productivity while also diminishing transaction costs. Such a
global computational sales and trading system allows increased
vendor competition. This, in turn, promotes multi-item competition
with minimized search costs.
[0058] Not only are prices made increasingly efficient by using
this system, but flexibility also is maximized since the system
allows customization functions as well, for single item sales or
for multiple-item packages.
[0059] The use of mobility by INAs further enhances efficiency and
flexibility by allowing increased automation convenience as well as
further opportunities to negotiate and execute transactions.
Mobility eliminates negotiation bias that may otherwise limit
operations to specific locations. Mobility also allows increased
failsafe computation processes because the participants are relying
less on (costly) communications systems that are prone to periodic
failure.
[0060] The integration of marketing and financial services provides
additional value to both buyer and seller. These services are fully
integrated into the system. The combination of promotions and risk
management options offers a push-pull approach to market incentives
in a distributed environment.
[0061] The use of AAs optimally leads to improved accuracy of
information, particularly benefiting the activities of INAs and
ITAs. The use of this information--both its analysis and timing--is
critical to the development of sustainable competitive
advantages.
[0062] The use of AI technologies automates the capture, analysis
and use of information and agents to be increasingly useful,
efficient and mobile.
[0063] Finally, because it is endowed with AI, the system is
self-organizing. As such, it is flexible, scalable and organic,
much like the economic systems it emulates.
DISCUSSION OF THE PRIOR ART
[0064] Distributed database architecture methods are disclosed in
Dao, U.S. Pat. No. 5,596,744; Baclawski, U.S. Pat. No. 5,694,593;
Clawson, U.S. Pat. No. 6,112,304; Singhal, U.S. Pat. No. 6,163,782;
Wolff, U.S. Pat. No. 6,067,545; and Sutter PCT/CA 00/55762. None of
these approaches include MASs applied to commercial purposes.
[0065] Database search technologies are described in several
patents that use ranking priority search techniques. These include
Nguyen, U.S. Pat. No. 5,444,823 (case-based); Kirsch, U.S. Pat. No.
5,659,732 (relevance score); Woods, U.S. Pat. No. 5,724,571
(relevance passage ranking); Herz, et al., U.S. Pat. No. 5,754,939
(frequency based ranking); Kirsch et al., U.S. Pat. No. 5,845,278
(relevance score); and Krellenstein, U.S. Pat. No. 5,924,090
(relevance priority). Other search approaches include Castelli,
U.S. Pat. No. 5,940,825 (adaptive similarity search); Prasad, U.S.
Pat. No. 5,960,422 (optimized source selection); Gable, U.S. Pat.
No. 6,029,165; Woolston, U.S. Pat. No. 6,085,175 (search agents);
and Williams, Jr., U.S. Pat. No. 6,108,686 (agent based information
retrieval). Distributed databases are searched using methods
described in Spencer, U.S. Pat. No. 5,826,261 (selective sharing)
and Hirsch, U.S. Pat. No. 5,978,799 (meta-search system). Object
database search approaches are described in Flowers et al., U.S.
Pat. No. 5,802,524 (parametric classification of attributes) and
Chipman et al., U.S. Pat. No. 6,037,868. Finally, two advanced
search approaches that use early generation genetic algorithms are
described in Takahashi et al., U.S. Pat. No. 5,706,497 (fuzzy-logic
inference pattern matching search generation) and Graefe et al.,
U.S. Pat. No. 5,822,747 (applies optimal plan to search relational
databases). None of these search approaches employ AI to access
object-relational distributed databases for adaptive filtered
search processing.
[0066] Data mining technologies can be classified into pattern
matching, collaborative filtering, database mining and data
analysis. Pattern matching is described in Taniguchi et al., U.S.
Pat. No. 5,764,975; and Agarwal et al., U.S. Pat. No. 5,819,266.
Collaborative filtering is described in Hey, U.S. Pat. No.
4,996,642; Heckerman et al., U.S. Pat. No. 5,704,017 (applying
Bayesian inference); Robinson (1), U.S. Pat. No. 5,790,426,
Robinson (2), U.S. Pat. No. 5,884,282 and Solomon, PCT 01/33464 A1.
None of these pattern matching or collaborative filtering
approaches use AI in distributed databases or apply these
approaches to a commercial MAS.
[0067] Database mining approaches are described in Simoudis et al.,
U.S. Pat. No. 5,692,107 (predictive model application); Agarwal et
al., U.S. Pat. No. 5,742,811 (GA applied to test candidate pattern
sequences); Chen et al., U.S. Pat. No. 5,758,147 (parallel data
mining); Kleinberg et al., U.S. Pat. No. 5,884,305 (rule-based
approach to relational database mining); Pham et al., U.S. Pat. No.
5,970,482 (application of intelligent agents to develop predictive
model); Mormoto et al., U.S. Pat. No. 5,983,222 (applying
association rule) and Bigus (2), U.S. Pat. No. 6,112,194 (user
feedback mechanism). Data analysis techniques are described in
Maeda et al., U.S. Pat. No. 5,761,389 (rule based analysis in
relational database) and Sheppard, U.S. Pat. No. 6,026,397. None of
these database mining or data analysis approaches use AI to access
distributed databases for the purpose of preparing or conducting
commercial activities in a MAS.
[0068] Information collaboration is discussed in Nakao, U.S. Pat.
No. 6,061,697 (SGML document management and collaboration);
Cornelia et al., U.S. Pat. No. 6,065,026 (multi-user document
authoring and sharing system); Brown et al., U.S. Pat. No.
6,067,551 (multi-user document editing system); Falkenhainer et
al., U.S. Pat. No. 5,930,801 (shared data system); Aditham et al.,
U.S. Pat. No. 5,941,945 (interest-based collaborative framework);
Fraenkel et al., U.S. Pat. No. 6,151,622 (document view
synchronization system); and Lo et al., U.S. Pat. No. 6,212,534
(distributed document collaboration). None of these information
collaboration approaches is used for commercial MTO customization
in a commercial sales and trade system.
[0069] Inter-agents are discussed in several patents, including
Klein et al., U.S. Pat. No. 5,499,364 (optimizing inter-agent
message flows); Bonnell et al., U.S. Pat. No. 5,655,081 (monitoring
and managing computer resources); Lagarde et al., U.S. Pat. No.
5,745,754 (intelligent sub-agent); Bauer, U.S. Pat. No. 5,877,759
(user/agent interaction interface); Kiraly et al., U.S. Pat. No.
6,088,731 (intelligent assistant applications); Huary, U.S. Pat.
No. 6,128,647 (applying arbiters to self-configuring messaging
system); Lange et al., U.S. Pat. No. 6,163,794 (user interface) and
Rothrock, U.S. Pat. No. 5,748,618 (data conferencing arbitration).
None of these approaches uses interagents in a systematic automated
way in a commercial MAS.
[0070] Commercial services involved in a distributed computer
system are described in Suarez, U.S. Pat. No. 5,790,789 and Meltzer
et al., U.S. Pat. No. 6,125,391. These approaches, however, do not
employ autonomous agents in a distributed commercial system.
[0071] Genetic programming is applied to search or agent technology
in several patents. For example, see Allen, U.S. Pat. No. 5,586,218
(autonomous learning agent); Gabriner et al., U.S. Pat. No.
5,848,403 (genetic algorithm scheduling system); Hunter, PCT U.S.,
97/44741 (combining multiple learning agents); Hughes, U.S. Pat.
No. 5,930,780 (distributed GP); Koza et al., U.S. Pat. No.
6,058,385 (simultaneous evolution of parallel computing);
Mayorga-Lopez, PCT U.S. 99/01262 (fuzzy inference applied to agents
for software retrieval); Dutton, PCT U.S. 99/05593 (software system
generation); and Liddy, PCT U.S. 00/63837 (evolving intelligent
agents to retrieve multimedia information). So far, none of these
genetic programming approaches have been applied to a commercial
MAS for the purpose of sales and trade.
[0072] Intelligent agents are described in Allen, U.S. Pat. No.
5,586,218 (autonomous agents); Schutzer, U.S. Pat. No. 5,920,848
(intelligent agents applied to financial transactions and
services); Carter, U.S. Pat. No. 5,926,798 (intelligent agents
applied to electronic commerce); Slotznick, U.S. Pat. No.
5,983,200; Frew, U.S. Pat. No. 6,009,456 (intelligent mobile agents
used for network-based information exchange); Devarakonda, U.S.
Pat. No. 6,055,562 (dynamic mobile agents); Hartnett, U.S. Pat. No.
6,064,971 (adaptive knowledge base); Paciorek, U.S. Pat. No.
6,065,039 (dynamic synchronous collaboration framework for mobile
agents); Kohn et al., U.S. Pat. No. 6,088,689 (multi-agent system);
Peckover, U.S. Pat. No. 6,119,101 (intelligent agents used for
electronic commerce); Luke, U.S. Pat. No. 6,131,087; Hodjat, U.S.
Pat. No. 6,144,989 (adaptive agent architecture); Bigus et al. (4),
PCT U.S. 98/43146 (intelligent agents applied to negotiation) and
Bigus et al. (5), PCT U.S. 98/47059. None of these approaches
applies to a sophisticated distributed demand-initiated commercial
MAS.
[0073] Automated negotiation or sales systems and methods are
described in Cragun, U.S. Pat. No. 5,774,868 (sales promotion
system); Kennedy, U.S. Pat. No. 6,055,519 (system for negotiation
and sales); Hoyt et al., U.S. Pat. No. 6,067,531 (automated
contract negotiator/generation system); Rickard et al., U.S. Pat.
No. 6,112,189 (apparatus for automating negotiations); Peckover,
U.S. Pat. No. 6,119,101 (intelligent agents for electronic
commerce); Luke, U.S. Pat. No. 6,131,087 (automatic matching of
buyers and sellers in electronic market); Bigus et al. (4), PCT
U.S. 98/43146 (intelligent agents applied to negotiation); Ojha et
al., PCT U.S. 00/33223 (automated transaction brokering system);
Tavor et al., PCT U.S. 00/43853 (automated virtual negotiations)
and Solomon, PCT WO 01/33464 A1 (customer demand-initiated system
and method for on-line information retrieval, interactive
negotiation, procurement and exchange). A reverse auction process
is described in Godin, U.S. Pat. No. 5,890,138. A disintermediated
auction system is described in Fisher et al., U.S. Pat. No.
5,905,974. None of these approaches employs AI in a (distributed)
demand-initiated commercial MAS.
[0074] A simple electronic aggregation system is described in
Halbert et al., U.S. Pat. No. 6,101,484. A simple combinatorial
auction method for determining a winner among multiple buyers for
multiple items from a single seller is described in Sandholm, U.S.
Pat. No. 6,272,473. A bundled asset trading system is described in
Stallaert et al., U.S. Pat. No. 6,035,287. These approaches fail to
show multiple seller winner determination methods as well as CA or
aggregation methods using AI in a distributed commercial MAS.
[0075] The present invention(s) go far beyond the systems, methods
or apparati described in the patents listed above. In order to
understand precisely how the present system advances the prior art,
we will present a description of the related art, including
commercial and academic systems, as well as a detailed description
of the drawings.
Detailed Description of the Prior Art
[0076] The first industrial revolution was characterized in the
eighteenth century by a shift from the small workshop production of
batches of products to the mass production process technologies of
assembly line factories. The second industrial revolution in the
early twentieth century evolved to the increasingly efficient
application of factory methods of production used by Henry Ford.
Late in the twentieth century, Toyota had further evolved Fordist
process technology by combining it with Just-in-Time (JIT)
production processes to create mass batch methods of efficient
manufacturing. The increasing use of robotics has allowed the
application of these manufacturing technologies far beyond the
production of cars.
[0077] The third industrial revolution has recently taken hold. For
this new phase of flexible production and distribution, information
has a fundamental role. Computing and communications systems of the
last generation have created major developments in production,
distribution and consumption.
[0078] One common characteristic of these industrial revolutions is
the quest for increasing automation, leading to greater
efficiencies, increased productivity, decreased costs and generally
more competitive businesses and markets. These automated
systems--often driven by complex software architectures--illustrate
the organizational constraints of commercial technology.
[0079] What is produced must be sold: An overproduction of items
creates an imbalance in the system, causing substantial disruption
to both consumption and production as represented in the pricing
system. Economic cycles are largely caused by these over- or
under-production processes. More precise information is needed to
identify and anticipate demand and optimize efficiencies, profits
and costs, while satisfying consumer needs.
[0080] In the early days of the nineteenth century, classical
economic theorists viewed economies mainly with an emphasis on
production systems (in contrast to consumption-based systems),
whereas the late nineteenth century neoclassical economists largely
viewed economic systems as driven by consumption. Such a
demand-based economic theory fits the model of a information
intensive economy.
[0081] What is needed is an adaptive, automated, information-rich
economic system for sales and trade that drives the production and
distribution of resources with maximum efficiency. This has been
the holy grail of automated commerce. In order to maintain market
efficiencies, competition needs to be maximized within the
constraints of a competitive marketplace. An optimal economic
system, while being demand-driven, is fair to buyer and sellers. It
will smooth out the business cycle when applied to general economic
consequences, and will be disintermediated and self-organizing.
[0082] So far, no system or combination of methods has fulfilled
the ideal of automating business processes promised in the
nineteenth and twentieth centuries. However, with the rise of
computer mechanisms, several attempts have been made to develop
simple systems that represent the early dirt roads in the
development of automated economic systems. These early models
include MAGNET, Fish Market, Kasbah and the Contract Net protocol.
All of these systems seek to automate the contracting component of
marketplaces.
[0083] FIGS. PA1, PA2, PA3, PA5 and PA6 outline these early models.
The first three systems involve centralized exchange processes.
Buyers and sellers meet at a specified time and location to bid for
items. When centralized exchanges are not always used, such as in
the case of the Fishmarket model, brokers are used to intermediate
the exchange of transactions. Only the Contract Net protocol--which
employs broker agents--is structured for distributed exchange
processes in which the parties to the transactions are in diverse
locations. All four main systems award contracts to victorious
buyers on the basis of price. All four processes also use an
ascending price buyer-side auction market model for competitive
bidding and a seller-based approach to winner determination.
Automated agents are used in all systems primarily to mimic the
behavior of buyers and sellers.
[0084] Numerous other systems exist. For example, in tete-a-tete
(FIG. PA4A) shopping agents and sales agents are proxies for
consumers and merchants, respectively, and employ bilateral
argumentation techniques of critique and counter-proposal (FIG.
PA4B). In another example, SWARM, economic relationships are
modeled. These main systems represent the early research in
automated agent-based commercial systems.
[0085] Two methods have been developed to automate existing
commercial exchange processes, including electronic data
interchange (EDI) (FIGS. PA7A and B) and electronic communications
network (ECN) (FIG. PA8). EDI simply computerizes manual billing
systems, while an ECN computerizes securities trading systems.
[0086] FIGS. PA9A and PA9B describe an intermediated
demand-initiated procurement system. This system is the first to
emphasize buyer-driven (reverse-auction) commercial transactions,
but is limited to intermediated exchange processes.
[0087] Most commercial relationships in advanced industrial
economies involve supply chain management (SCM). As these
relationships become increasingly complex and automated, SCM
technology involves e-sourcing software to assess and award bids to
suppliers on the procurement side as well as customer relationship
management (CRM) software on the buyer side. Enterprise resource
planning (ERP) software runs the internal business processes, such
as finance, accounting, human resources and manufacturing control.
FIG. PA10 illustrates a traditional approach to the integration of
SCM, ERP and CRM software technologies in a supply chain. It uses
the example of a big company that produces goods or services
intermediating between small company sellers and buyers.
[0088] Increasingly, database management systems (dbms) represent
the backbone of commercial software systems. In the past twenty
years, most dbms have used relational database architectures
developed by IBM and others. However, newer dbms involve the
ordering of objects such as tables of data-sets. Most contemporary
dbms involve a fusion of object-relational (o-r) architectures. A
traditional o-r dbms is described in FIG. PA11. Software
agents--specifically, spiders--collect data from various sources
into a central depository. Queries are directed to the centralized
database, which produces a prioritized list of responses. The
limitations of this system include problems with time-sensitive
data (because the inputs are necessarily dated) and organizational
method dependency (because the results depend on the way the data
was input, which may not fit the appropriate solution to the
original query).
[0089] Distributed search technology, shown in figure PA12, was
developed to respond to the shortcomings in the traditional search
approach. Rather than collect data into a central depository, a
distributed dbms searches numerous databases in real time. Assuming
that the translation between the systems reveals compatibility of
inter-communication, an initial query is sent to various databases,
and search results are prioritized according to specified criteria
for ordered display. Most search technology involving the Internet
uses some combination of central and distributed technology
approaches.
[0090] FIG. PA13 refers to a traditional aggregation system in
which customers pool together in order to acquire a specific
product. The aggregation process allows a vendor to provide
wholesale discount pricing, upon which, after a specified time, the
buyers and seller ultimately agree. The aggregation process
automates a wholesaler intermediary function, in order to clear
markets, but it has been increasingly challenged in the Internet
age because supply chain layers are more easily eliminated and more
information is available in a distributed computer network.
[0091] FIG. PA14 refers to a simple method of providing
intermediated option contracts. This process allows a vendor to
hedge a risk by selling an item to a second party even after it has
initially agreed to sell it at a specified price to a first party.
Although the seller is obliged to pay the first party a pre-agreed
penalty if the seller exercises a contingency to sell the item to
another party, the seller can make more money--thus maximizing its
benefits--by paying the penalty and selling to the second party for
a greater profit than the first price plus the penalty.
Increasingly, finance and trading firms must utilize these risk
management strategies in complex ever-changing markets so as to
maximize revenue and optimize profits.
[0092] Though these examples of prior art point primarily to
academic research, which has a more established history, aspects of
these systems are becoming increasingly patentable, whether in
agent, database, search, negotiation, auction or sales categories.
A discussion of the patent prior art literature involving these
important categories can be seen in the Summary of the Invention. A
reference to the literature on the prior art can be viewed in the
bibliography.
BRIEF DESCRIPTION OF THE LIST OF FIGURES
[0093] Prior Art: The first fourteen figures illustrate aspects of
the prior art.
[0094] FIG. PA1 is a schematic diagram of the operation of a
multi-agent system.
[0095] FIG. PA2 is a schematic diagram of an agent-based
contracting system.
[0096] FIG. PA3 is a schematic diagram of an agent marketplace.
[0097] FIG. PA4A illustrates a simple system for integrated
negotiation.
[0098] FIG. PA4B shows a method for bilateral argumentation.
[0099] FIG. PA5 is a schematic diagram of several stages of a
contracting system.
[0100] FIG. PA6 is a flow chart of the Contract Net Protocol.
[0101] FIG. PA7A is a schematic diagram of EDI as a paper
replacement technique.
[0102] FIG. PA7B is a schematic diagram of EDI as a process
elimination technique.
[0103] FIG. PA8 shows a simple electronic communications network
(ECN) system.
[0104] FIGS. PA9A and B are schematic diagrams of an intermediated
demand-initiated procurement system.
[0105] FIG. PA10 is a schematic diagram of a traditional supply
chain with customer and supplier relationships.
[0106] FIG. PA11 illustrates a traditional search method in an
intermediated network system.
[0107] FIG. PA12 illustrates a distributed search process in a
disintermediated network system.
[0108] FIG. PA13 illustrates a traditional aggregation method.
[0109] FIG. PA14 illustrates a method for intermediated option
contracts.
THE SYSTEM
[0110] FIG. 1 is a schematic diagram showing the architecture of a
cooperative communications network (CCN).
[0111] FIG. 2 is a schematic diagram describing the relationships
between the layers of a CCN system.
[0112] FIG. 3 is a schematic diagram of a showcase database
system.
[0113] FIG. 4 illustrates multiple vertical databases.
[0114] FIG. 5 is a schematic diagram of a showcase database.
[0115] FIG. 6 is a schematic diagram of showcase database
operation.
[0116] FIG. 7 is a schematic diagram of showcase data flow.
[0117] FIG. 8 is a flow diagram of the inter-agent system
architecture.
[0118] FIG. 9 shows how rivers of data flows in a CCN operate.
[0119] FIG. 10 is a schematic diagram of commercial search agent
(CSA) system architecture.
[0120] FIG. 11 is a flow diagram of a CSA first query.
[0121] FIG. 12 is a schematic diagram of CSAs indicating search
priorities.
[0122] FIG. 13 is a schematic diagram of a CSA method used as an
initial commercial search request.
[0123] FIG. 14 is a schematic diagram of CSA filtering methods.
[0124] FIG. 15 illustrates an apparatus for disseminating
promotions in the system.
[0125] FIG. 16 is a schematic diagram illustrating proximity
marketing for mobile INAs.
[0126] FIG. 17 shows the promotional discounting process.
[0127] FIG. 18 shows a dynamic pricing model with adaptive peak and
off-peak pricing along a product or service cycle.
[0128] FIG. 19 shows a method for pricing discount promotions.
[0129] FIG. 20 illustrates promotions integrated with the CSA and
Showcase database.
[0130] FIG. 21 is a schematic diagram illustrating a process of
risk management option (RMO) contracts in a distributed network
system.
[0131] FIG. 22 is a schematic diagram showing processes for
transaction contingency logistics in a distributed contracting
system.
[0132] FIG. 23 is a schematic diagram of information collaboration
in a distributed network system architecture for use with
made-to-order customization.
[0133] FIG. 24 is a schematic diagram illustrating a collaboration
process for made-to-order (MTO) customization.
[0134] FIG. 25 is a schematic diagram of intelligent negotiation
agent (INA) interactions in a multi-agent system with an emphasis
on buyer agent (b-INA) and seller agents' (s-INAs) interaction.
[0135] FIG. 26 is a flow diagram representation of sequences of INA
interactions.
[0136] FIG. 27 is a schematic diagram of a method for
pre-negotiation in a multi-agent system.
[0137] FIG. 28 is a flow diagram representation of a method for
time-based concealment of negotiation strategies in a distributed
contracting system.
[0138] FIG. 29 is a schematic diagram of a system of INA
logistics.
[0139] FIG. 30 is a flow diagram representation of a method for INA
interaction.
[0140] FIG. 31 is a schematic diagram of a method for INA
interaction.
[0141] FIG. 32 is a schematic diagram of an INA architecture
emphasizing the initial interactions.
[0142] FIG. 33 is a flow diagram representation of an INA system
architecture emphasizing negotiation interactions.
[0143] FIG. 34 is a schematic diagram of time-based negotiation
sequences.
[0144] FIG. 35 is a schematic diagram of a method for initial INA
mobile location protocol settlement.
[0145] FIG. 36 is a flow diagram representation of a tournament
configuration of INA winner determination.
[0146] FIG. 37 shows multivariate negotiation methods.
[0147] FIG. 38 shows automated negotiation sequences for item
attributes with preestablished parameters.
[0148] FIGS. 39A and B is a schematic diagram of a demand-initiated
automated negotiation in a distributed system illustrating
mobility.
[0149] FIG. 40 shows multilateral distributed competition as a
competitive double shout negotiation process.
[0150] FIG. 41 illustrates an INA negotiation module, including a
schema of negotiation methods.
[0151] FIG. 42 illustrates an INA auction module, including a
listing of several auction types.
[0152] FIG. 43 is a schematic diagram revealing the interactions of
the INA negotiation module with the INA auction module.
[0153] FIG. 44 illustrates the pricing strategy module in the
context of interactions with AAs.
[0154] FIG. 45 illustrates the interaction dynamics of INA
"personalities".
[0155] FIG. 46 is a schematic diagram of a system for the
interaction of neutral cooperative INAs, including intermediation
and aggregation applications.
[0156] FIG. 47 is a schematic diagram showing the sources of a
C-INA transaction initiation.
[0157] FIG. 48 is a flow diagram representation of a method for
B-C-INA based aggregation.
[0158] FIG. 49 illustrates several main automated aggregation
category structures in a distributed network system.
[0159] FIG. 50 shows an INA based mass pooling approach to
aggregation.
[0160] FIG. 51 illustrates a disintermediated aggregation method
using C-INAs.
[0161] FIG. 52 is a schematic diagram of a disintermediated
aggregation method for mass customization.
[0162] FIG. 53 is a flow diagram representation of a method for
using dynamic INAs as double agents for arbitrage applications.
[0163] FIG. 54 illustrates an intermediated method for performing a
combinatorial auction (CA) between a single seller and multiple
buyers.
[0164] FIG. 55 illustrates a method for performing a CA with INAs
between multiple buyers and multiple sellers in a single
session.
[0165] FIG. 56 illustrates a method for winner determination of an
interactive multilateral auction in a final session.
[0166] FIG. 57 is a flow diagram representation of a method for
filtering variables for multi-item CAs.
[0167] FIG. 58 is a flow diagram of a disintermediated method of
multi-item bidding from one seller to multiple buyers.
[0168] FIG. 59 is a flow diagram of a disintermediated method of
multi-item bidding between multiple sellers and a single buyer.
[0169] FIG. 60 is a schematic diagram of a disintermediated method
of multi-item aggregation of pre-set bundles between multiple
buyers and multiple sellers.
[0170] FIG. 61 is a schematic diagram of a disintermediated method
of multi-item bidding between multiple buyers and multiple
sellers.
[0171] FIGS. 62A and B is a flow diagram representation of a
disintermediated method of aggregation whereby multi-item bundles
are exchanged between multiple buyers and multiple sellers using
c-INAs.
[0172] FIG. 63 is a flow diagram of a disintermediated method for
conducting arbitrage of multi-item bundles between multiple sellers
and multiple buyers using d-INAs.
[0173] FIG. 64 illustrates multi-factorial bidding approaches by
listing item variables that can be sorted.
[0174] FIG. 65 shows a multi-feature example of item-factors that
can be sorted in the case of personal computer configurations.
[0175] FIG. 66 shows examples of multi-item bundle category
applications.
[0176] FIG. 67 is a schematic diagram representation of a seller
mobile transaction agent (s-ITA) system architecture.
[0177] FIG. 68 is a flow diagram of s-ITA operation.
[0178] FIG. 69 is a schematic diagram of a s-ITA and b-ITA system
process in the final negotiation with one seller.
[0179] FIG. 70 shows the ITA service categories, including the
buyer and seller roles.
[0180] FIG. 71 describes ITA service categories.
[0181] FIG. 72 is a flow diagram representation of b-INA
micro-agents generated particularly for a negotiation session in a
mobile application.
[0182] FIG. 73 is a flow diagram representation of a method for
genetic algorithms to be applied to multi-agents system.
[0183] FIG. 74 is a flow diagram representation of a method for
neural networks applied to a multi-agent system.
[0184] FIG. 75 is a flow diagram of a genetic programming system
process.
[0185] FIG. 76 is a schematic diagram representation of methods for
a genetic programming learning schemas.
[0186] FIG. 77 is a flow diagram representation of a method showing
evolutionary computation applications to autonomous agents.
[0187] FIG. 78 is a schematic diagram showing AI applied to agency
in a distributed system.
[0188] FIG. 79 is a flow diagram of an evolutionary computation
architecture with AA and INA applications.
[0189] FIG. 80 is a flow diagram illustrating layers AI for optimum
agent mobility.
[0190] FIG. 81 is a schematic diagram showing AA architecture from
buyer and seller viewpoints.
[0191] FIG. 82 is a schematic diagram showing kinds of data
analysis and synthesis.
[0192] FIG. 83 is a schematic diagram representation of an AA data
flow process.
[0193] FIG. 84 is a schematic diagram representation of methods of
data mining, with emphasis on interactions between a commercial
search agent (CSA) and AA.
[0194] FIG. 85 is a flow diagram representation of methods for
collaborative filtering for cross-marketing recommendations
applications.
[0195] FIG. 86 is a schematic diagram of b-AA operation with
mobility.
[0196] FIG. 87 lists the variables of a super-score system in a
multi-agent system.
[0197] FIG. 88 lists the variables of market and economic analytics
in a multi-agent system.
[0198] FIG. 89 lists the variables of an accountability index in a
multi-agent system.
[0199] FIG. 90 lists the variables of a financial criteria index in
a multi-agent system.
[0200] FIG. 91 lists insurance risk factors for use in a
multi-agent system.
[0201] FIG. 92 lists service categories in a multi-agent
system.
DETAILED DESCRIPTION OF THE MAIN EMBODIMENTS
[0202] The system represented by the present invention has numerous
distinctive embodiments. The present disclosures illustrate in
detail the main ideas of the invention and are not intended to
restrict the invention to a single embodiment.
[0203] The system and methods incorporated in the present invention
are implemented by using software as applied to networks of
computers, microprocessors or mobile computers. Software is stored
in memory on computer disk drives. The microprocessors in the
computer hardware use database software to store data used by
software applications such as intelligent software agents. Agents
are computer software program code that can be activated to perform
specific functions. Once activated, agents can be executed in a
node of a computer network, or can move from node to node to
manifest mobility.
[0204] The present invention, or cluster of methods, aims to solve
problems in the area of computation for automating demand-initiated
sales processes in a distributed network. Specifically, the present
invention uses a distributed database system to automatically store
data about goods and services, and access, analyze, and collaborate
about the data.
[0205] The present invention further discloses a demand-initiated
sales application of intelligent software agents in a multi-agent
system (MAS). The agents perform an automated, multivariate
negotiation for individual items in a multilateral interactive
environment between a buyer and at least two sellers. The system
further uses intelligent negotiation agents (INAs) to perform
aggregation, arbitrage and combinatorial functions in a MAS.
Negotiation functions are supplemented by intelligent transaction
agents (ITAs) that clear transactions and offer services. One key
feature of the software agents in a distributed MAS is the use of
mobility. Such a computer system applies the frontiers of
artificial intelligence.
CCN and Showcases
[0206] The foundation of the present system is the commercial
communications network (CCN) architecture illustrated in FIG. 1. A
showcase is a database that contains constantly updated information
from a corporate database. Corporate databases (1005) receive data
inputs in real time as seller interagents (S-IAs) (1015), filter
information for seller showcases (1045). Analytical agents (AAs)
for both seller (1010) and buyer (1013) analyze and filter
information: seller AAs (S-AAs) analyze market data inputs for
seller inter-agents (S-IAs) while B-AAs analyze market data inputs
for B-IAs. Data used can be objects, codes, text, images,
multimedia or other formats.
[0207] On the seller side, promotions and risk management option
(RMO) contracts are provided to the showcase by the S-IAs from
promotion and RMO modules (1020).
[0208] On the buyer side, buyers use B-IAs by setting the
parameters of software preferences at the customer graphic user
interface (GUI) (1040). The GUI can be a multimedia intensive
model. B-IAs are intermediary software programs that interface with
the customer GUI and other agent programs to constantly re-adjust
the parameters of automated software agents.
[0209] In one embodiment, buyer and sellers can bypass the showcase
system and collaborate (1035) B-IA and S-IA interactions in order
to mutually identify item specifications prior to negotiation
sessions. Once the collaboration process is employed, the CSA can
access showcase databases for a first search query and then proceed
to a negotiation session.
[0210] Once a B-AA has analyzed a product or service category, this
information is forwarded to a B-IA, which then initiates a search
of showcases by activating a commercial search agent (CSA) (1060).
After a first search query, the B-IA can use the search results to
interact with S-IAs in a pre-negotiation session (1055), which sets
the rules of encounter for negotiation sessions between the buyer
and two or more sellers.
[0211] After the pre-negotiation session has established initial
parameters, the Buyer Intelligent Negotiation Agent (B-INA) (1085
and 1090) enters into interactive multi-lateral negotiations with
at least two seller intelligent negotiation agents (S-INAs) (1075
and 1080). S-INAs use pricing, negotiation, and auction modules to
automate the negotiation interactions with the B-INA. Once an B-INA
is ready to complete a transaction and select a winner, it sends
the transaction to a buyer intelligent transaction agent (B-ITA)
(which uses data from an S-AA) to check the terms of the
transaction. While this is occurring, other negotiations may stop
or continue depending on initial negotiating parameters. If a buyer
cannot satisfy the conditions of the transaction, the S-INA sends
the buyer back to its S-ITA to renegotiate that issue. Similarly,
the B-ITA must clear the transaction for the B-INA by
double-checking and clearing all terms of the S-INA. Once all terms
are mutually agreed upon, the transaction is concluded and all
inter-INA activity terminates. ITAs in turn update their respective
AAs with data from the transaction.
[0212] FIG. 2 describes the CCN system layers and their
relationships.
[0213] The layers of a CCN can be described as follows: (1)
Corporate databases and customer GUI are the front end or top
layer; (2) showcase databases are at the second layer; (3) AAs,
both buyer and seller, as well as promotions and RMOs, are at the
third layer, which represents analytics; (4) Buyer and seller
inter-agents and collaboration are in the middle, interagency
layer, at layer four; (5) CSAs are at the fifth (search) layer; (6)
Several varieties of INAs are at the sixth (negotiation) layer,
and; (7) Buyer and seller ITAs are at the seventh (transaction)
layer. The configuration of this distributed database
infrastructure and this integrative multi-agent system (MAS)
differs substantially from prior systems, as does the system
architecture described in these figures. The specific methods for
actions and interactions also vary from earlier approaches. The MAS
is integrated into the database system; the database system
configuration provides the forum for the MAS.
[0214] FIG. 3 illustrates a showcase database system whereby
corporate databases (1155) are using analyses from S-AAs (1180) as
well as receiving corporate data inputs. S-IAs (1157) use S-AAs to
analyze and filter data in order to continuously place new products
and services in each respective company showcase database (1160).
Similarly, showcase data is continuously purged to reflect the most
current commercial activities. S-IAs also constantly receive data
from promotion and RMO modules (1165) (also informed by S-AAs).
Showcases thus reflect the most current data on products and
services, and are informed by market data; as market data changes,
the showcase data constantly updates to respond.
[0215] Showcases typically operate in the context of specific
industries. See FIG. 4. Showcases are more focused in vertical
industries and tend to share common specialized languages (both for
items and the codes (e.g., dictionaries) to which they refer). Each
vertical showcase system is similar to the yellow pages, but for
worldwide access to an industry.
[0216] In an additional embodiment, a CCN can overlap in more than
one industry. Such horizontal (or trans-vertical) showcases combine
multiple categories of items and can be accessed by multiple
showcase systems. In another embodiment, a CCN can be customized to
a specific business, such as a very large corporation that may use
thousands of vendors or customers.
[0217] Such a showcase database system has advantages over other
systems. First, the original corporate database is protected and
inaccessible to outsiders. Second, the system sets up a complex of
similarly focused micro-databases that are isolated for remote
commercial access. Third, since the system is distributed, access
to information in real time is more current. Fourth, the system is
overseen by a cooperative of its own members (rather than one
member) and so each member company is responsible for its own
showcase as well as aspects of the whole system. Fifth, since each
showcase is constantly updated, its data is more complete and
accurate. Sixth, this CCN architecture is scalable. Seventh, the
system is structured to be omni-directional and thus accessible
from anywhere. Finally, because promotions (and RMOs) are
integrated into the showcase system, marketing (push and pull) is
key to its pragmatic operation.
[0218] FIG. 5 illustrates how S-AAs feed filtered data to S-IAs
(1325) for inclusion into a showcase. S-AAs (1310) filter data from
market sources (1300) to a showcase (1345). Though data included in
showcases is primarily derived from corporate databases (1320),
data is also input from the pricing module (1330), promotion module
(1335) and RMO module (1340). Further description of these modules
are made in FIGS. 14-22 and 41-44. At any given moment, the
showcase database view will be different since it represents
snapshots of constantly moving data streams that can be seen over
time (1315). Finally, a showcase can be accessed from various
locations (1350) by CSAs.
[0219] As illustrated in FIG. 6, corporate databases (1340) provide
raw data to S-IAs (1344). In addition, S-IAs receive data from
S-AAs (1346) that filter raw market data from various sources
(1342) as well as promotion and RMOs (1350). The S-IA continuously
filters the data in real time to the showcase database (1348).
[0220] The showcase has two main components: (1) a database for
specific items and (pre-set) bundles and (2) a database for
customizable items in the collaboration process. Since data in the
showcase is time sensitive, such data is constantly renewed and old
data is purged. The CSA (1352) can access the showcase for the
initial query in order to initiate the negotiation process.
[0221] FIG. 7 shows the flow of data from the corporate database
and S-AA (1355) to the S-IA (1357), the importation of data into
the showcase (1360) and the continual updating and purging of
showcase data (1362) that leaves the showcase with current data
sets (1365).
[0222] Inter-agents (IAs) are used as mobile intermediaries between
various agents and databases (or GUIs). IAs are categorized as
either buyer-side or seller-side. IAs also interact with each
other.
[0223] FIG. 8 describes inter-agent system architecture. On the
seller side, a seller (1370) interfaces with a corporate database
(1374) via a GUI (1376). The seller inter-agent (S-IA) (1382)
intermediates between the corporate database and the showcase(s)
(1390) as well as the S-AA (1386).
[0224] The buyer inter-agent (B-IA)(1384) intermediates between the
customer-side GUI (1378) and the CSA (1388) and between the GUI and
the B-AA (1386).
[0225] In one embodiment, IAs can be used more expansively to
include interaction with INAs, ITAs and market data.
[0226] Because they are intermediaries, IAs may be mobile. Their
locations change in sequence or they may alternate.
[0227] FIG. 9 illustrates the rivers of data flows in a CCN system.
This figure shows the seller and buyer sides as well as the top
(database and interagent) layers and the bottom (negotiation and
transaction processing) layers. Note that most actions in this view
of the CCN system involve interactive (i.e., not unidirectional)
functions. This view also downplays the primary negotiation
functions to emphasize the supporting structure.
CSAs
[0228] Showcase databases are accessed by a commercial search agent
(CSA) as illustrated in FIG. 10. The CSA (1535) is informed by a
B-AA (1540) in order to focus the search. In addition, promotions
(1530) target, and invite, CSA searches. The CSA accesses the
showcase databases as continuously looped queries (1550). Since the
showcases are continuously updated, each search is accurate and
fast.
[0229] FIG. 11 shows the sequence of CSA actions in the first
query. (1) Market data (1552) is input into B-AAs (1620). (2)
Various promotions (1585, 1590, etc.) are input into showcases via
S-IAs. (3) A CSA (1625) accesses a B-AA and, with data from the AA,
the CSA accesses the showcases, which have been informed by
promotions, in an orderly sequence. The showcases are constantly
updated by the promotional modules. The CSA filters the best
promotions and then accesses selected showcases, which respond by
providing specific data. FIG. 84 shows CSA and AA interactions with
an emphasis on data mining approaches.
[0230] The CSA asks a specific question in order to receive
specific commercial data from a showcase. Key words of the first
CSA query are ordered and ranked so as to provide a feedback in a
particular sequence.
[0231] Once several showcases respond with commercial data, and the
data is ranked according to user specified priorities, the data is
provided to B-IAs (1630) in preparation for negotiation sessions
(1635).
[0232] FIG. 12 shows the flow of sequences involving the CSAs
search method. Once a CSA is activated (1700) and makes a first
search query (1710), it uses filters (illustrated in FIG. 14) to
determine search priority factors (1720). The user can configure
these factors by selecting search parameter preferences. The search
process proceeds by sorting the preferred attribute (1730).
[0233] The CSA then accesses (1740) various showcases in a CCN (or
in more CCNs) according to registry code sequences. The showcases
are accessed according to a time-sequence synchronization that
provides code-priority to specific showcases based on factors upon
which CCN co-op members can agree. An efficiency optimization
process calibrates the synchronization access system. Such
priorities that can be conferred on a CSA include popularity,
specialty, price, quality, etc.
[0234] The CSA sends out a code seeking a match to a request for
specific attributes to the showcases in a CCN (1750). Upon receipt
of the code request, the showcase database(s) respond (1760) by
sending back matching relevant query attributes. The CSA then
orders and ranks responses according to configured preferences
(1762). The CSA then registers the search results to the buyer GUI
(1764) or to B-IAs or B-INAs.
[0235] In FIG. 13, the CSA (1805), while being informed by a B-AA
(1800), requests information for initial item data from each of
several showcases. In this example (preferred embodiment), showcase
3 (1815) allows the customers to buy the item at an initial price.
However, the buyer can negotiate with at least two (1820 and 1825)
sellers by proceeding to a pre-negotiation session (1830), which
sets the terms of the negotiation process. Direct interactive
bilateral negotiation proceeds between a B-INA (1845) and at least
two S-INAs (1840 and 1850). As illustrated in the example in FIG.
10, in the case of S-INA2, the negotiation session proceeds, but in
the case of S-INA1, the buyer may choose to buy the item at an
initial price (1860) and proceed to closing the transaction with an
ITA (1880). In any event, the CSA represents the first request for
information, including item features, terms and price.
[0236] The CSA can filter information about commercial items
according to various categories including price, location, item
niche, availability, bundles, accountability, and past experiences.
FIG. 14 illustrates these CSA filters (1910). The accuracy and
quality of data are also filtered and purified (1965). These
filters are manually configurable so as to track the specific
categories, either individually or in combination.
[0237] Just as buyers can configure CSA filters, sellers can invite
specific buyers to special promotions by micro-casting unique
discount or incentive opportunities (1920). Taken as a whole, these
seller-driven inducements create a "demand-shaping" of distinct
buyer needs (1930). By influencing buyer activity, the seller can
control its own suppliers more smoothly and even out buyer demand.
Such demand shaping occurs at the CSA and promotional level. Once
data is purified and analyzed for buyer preferences, the
promotional (& RMO) data is used by buyers to generate choices
(1970) for negotiation sessions.
Promotions and RMOs
[0238] Promotions can be pushed to customers from sellers through
customized and broadcasting processes. FIG. 15 shows that a
customer can detail preferences (2015) at the CSA after obtaining
general item data from a broadcast promotion (2010). The customer
then accesses the CSA (2025), which, in turn, accesses the
showcases (2030) in a specific industry. Showcases can receive
targeted ads (2035) based on pre-specified preferences, which are
accessed by a next data stream of the CSA. Finally, the CSA makes a
buyer request for sellers' information (2020).
[0239] Because the system contains a layer of mobility and because
mobility is location-dependent, proximity marketing--in which a
promotion is contingent on a specific point in a space-time
matrix--is used as illustrated in FIG. 16.
[0240] After moving from an initial negotiation (2040) at a
specific location, a B-INA moves to another location (2042). An
S-INA accesses a database via an S-AA to determine whether the
B-INA qualifies for a specific promotion (2046) at that time. If
the B-INA does not qualify, no promotion is provided (2048). On the
other hand, if the B-INA does qualify, the S-INA offers it a
specific, time-sensitive promotion at a specific location (2050) by
accessing the promotion module (2044). These promotions can be in
the form of "exploding" offers that diminish over time until a
deadline is realized and they expire.
[0241] Proximity marketing allows sellers to shape demand by
influencing buyer behavior with incentives. These incentives may be
induced by the seller because it receives unexpected reductions
from its suppliers. The changed circumstances induces the seller to
offer improved terms by shifting opportunity to other, more
available, supplies to buyers. Proximity marketing also allows
sellers to exploit opportunities by immediately offering promotions
for qualified buyers in the context of a demand-initiated sales
system. Such a process may confer an advantage on a particular
seller. One of the advantages of proximity marketing is that
sellers can offer an incentive to a B-INA at a specific place in
order to induce the B-INA to conduct negotiations at the home of an
S-INA. The location priority would confer an advantage on the S-INA
by providing easy access to computation resources. Such proximity
marketing with mobility in a distributed MAS not only enhances
revenue management but also profit maximization through increased
efficiencies.
[0242] There are different main types of promotions as outlined in
FIG. 17. In general, products that use decaying technology (2060)
and services that use off-peak capacity (2070) are discounted. See
also FIG. 18. In addition, items that are bundled, possess fewer
features or lower quality, have multiple units or are less
time-sensitive (2080), are discounted. Information about these
discounts is broadcasted in the form of promotions outlining these
sales opportunities.
[0243] A Dynamic pricing curve is illustrated in FIG. 18. This
figure shows the decline of prices related to off-peak services or
to trailing edge technology.
[0244] FIG. 19 shows a list of promotional categories to which item
prices tend to fall. These categories are thus used in marketing
promotions by the promotion module.
[0245] Promotions are typically included at the front end of the
system, at the CSA or showcase level. As FIG. 20 illustrates,
promotions can come in the form of ads or inducements. Ads are
pushed as broadcasts or targeted as customized. Inducements, on the
other hand, are invited and driven by the seller, or intentionally
requested by a buyer. Discounts are again specified according to
the product decay curve (or by supply and demand curves) or
services yield management curve. It is important to note the
multivariate nature of discounting beyond the price factor alone,
because quantity, quality, features, bundles, and time-sensitivity
are each important criteria that affect customer demand. In fact,
these multi-faceted item criteria allow promotional cross-
marketing for sellers to provide marketing opportunities before or
during an initial search.
[0246] Risk management option (RMO) contracts represent a
seller-induced opportunity to control risks by offering buyers
contract clauses that penalize sellers. While promotions are pushed
to buyers from sellers, RMOs are "pulled" by buyers from sellers.
These contingencies allow sellers to hedge their bets on items that
may not be fully in their control. On the other side, buyers can
exploit these risks as opportunities, either to get good value, or
to receive a penalty from the seller in the event that they must
withdraw from a contract by exercising a contingency clause.
Because the terms of RMOs must be agreed to early in the
transaction, they are on the level of a promotion. Further, like
promotions, since the underlying circumstances for sellers
constantly change, RMOs conditions and contingencies constantly
change. Therefore, as a CSA accesses a showcase, the most recent
promotional and RMO data is constantly updated. Since showcases are
structured in a distributed system, RMOs are similarly
distributed.
[0247] FIG. 21 outlines the operation of RMO contracts in a
distributed system. At (2210) a seller (S1) agrees to sell item(s)
at specified terms to a customer (C1), while S1 agrees to pay a
specified penalty to C1 if it cannot comply with the terms of the
transaction. Seller AAs (2235) are constantly receiving market data
inputs (2215) and dynamic pricing data inputs while assessing risk
priorities and preferences (2245). Being an opportunist and
rational agent, S1 constantly seeks a better deal than he arrived
at with C1. At (2230), S1 finds C2 and enters into a new
arrangement with different terms. S1 then commits to buying the
item from S2 in order to re-sell to C2, according to optimal
terms.
[0248] S1 sells the item(s) to C2 (2240) at a higher price than
original terms arranged with C1; thereby, at (2265), C1 receives a
penalty from S1; either the item is merely delayed, which may
warrant a mild penalty (2295) or cancelled, which will typically
justify a more significant penalty (2300).
[0249] In the meantime, because the risks of getting squeezed
between a buyer obligation and seller obligation can cause problems
if a catastrophe occurs in the supply chain, sellers swap risks
with other sellers to diversify or concentrate risk outcomes. Risks
are either re-packaged (2260) or time shifted (2290). If they are
re-packaged, the risks can be either concentrated (2255) (for
maximum return on the upside) or diluted (2310) for risk
diversification (minimum loss on the downside). By time shifting
various risk contingencies, risks can be diluted over time so as to
overcome a temporary supply imbalance. Risk penalties can come in
the form of cash or future discounts (coupons).
[0250] Once a buyer is terminated from a contract at the time of
seller initiation, the buyer is free to begin the process again.
However, since circumstances constantly change and data is
constantly updated, an identical transaction is unlikely to be
repeated without modification. RMOs allow sellers flexibility,
especially from unforeseen supplier circumstances, but also provide
buyers with distinct market opportunities. RMOs function as a sort
of risk arbitrage by shifting risk in unforeseen circumstances. The
mobility aspect of the present system allows unique advantages in a
distributed network because sellers are able to more accurately and
immediately respond to complex market circumstances up and down the
supply chain. Finally RMOs, when combined with promotions, provide
a powerful marketing integration component to a transaction
system.
[0251] A reverse RMO may be used as a form of performance bonus to
reward a seller for an excellent job such as accelerated contract
terms favorable to a buyer. A compounded RMO may also be used by
D-INAs for arbitrage applications. Such complex RMOs are used by
the original seller to the D-INA in its initial role as buyer and
then by the D-INA (in its secondary role as seller) to a buyer. As
an intermediary, the D-INA effectively spreads the risk from first
buyer to final seller.
[0252] Agents do not only have the power to negotiate and contract,
but also to use contingencies, by both buyers and sellers and with
penalties and without penalties (depending on the nature of the
contingencies). In FIG. 22, the logistics of transaction
contingencies are shown. In order to focus negotiations with one
seller, a buyer may delay a seller negotiation for a time (2319).
One way to do this is for a buyer and seller to express an interest
in a transaction (2320). Before closing the transaction, a B-ITA
seeks to close the transaction (2323). The buyer can end the
transaction by withdrawing (2335) and seek to negotiate for other
items (2337) given buyer priorities. Alternatively, if a buyer is
not qualified (2331) according to an S-ITA, the seller may withdraw
without penalty (2333).
[0253] The buyer and seller then agree to enter into a transaction
(2321 and 2324). If the seller withdraws from the transaction, the
seller activates an RMO contingency (2326) and the seller pays a
penalty (2327). If the buyer contingency is not performed (2325),
the buyer may withdraw with a penalty (2329). In this case, the
buyer may return to the negotiation session and seek his second
best choice (2337). These choices and contingencies can be
performed at various locations in the distributed network by the
various agents. In addition, each phase in the process can be
performed with mobility at alternating locations in a further
embodiment.
Collaboration
[0254] Increasingly, beyond promotional and RMO marketing schemata,
interacting businesses need to collaborate on the specificity of
complex items prior to an initial search. Particularly for unique
or custom items, collaboration between a buyer and at least two
seller competitors is key to describing the item so that all
parties are clear on the specifications before beginning the
negotiation process. In fact, the negotiation process itself may
involve the give-and-take not only of pricing, but also detailed
description. In an era of lean and Just-in-time (JIT)
manufacturing, made-to-order (MTO) processes require collaboration
with buyers and sellers. Since collaboration can be initiated early
in the sales process, and since collaboration is fundamentally
informational, the information collaboration for MTO items,
illustrated in FIG. 23, is integrated with the CSA during an
initial search, as well as before and after a search. Collaboration
can occur before the CSA's first query (by a B-IA and S-IAs), and
after the CSA query at the pre-negotiation stage.
[0255] FIG. 23 shows the initial collaboration between a B-IA and
at least two S-IAs. AAs inform both buyer (2365) and seller
inter-agents (2370 and 2385). Once initial collaboration has
occurred and comparable item specifications identified and input
into a showcase, the buyer, via the inter-agent, accesses the CSA
(2400) in order to get pricing and transaction term data from
sellers. The initial collaboration process bypasses a pre-set item
showcase database input, and precedes a search query. During a CSA
search (2400), on the other hand, collaboration occurs by the CSA
interacting with S-IAs (2420 and 2430) and a B-IA (2433); S-IAs
interact with both showcases (2410 and 2445) and S-AAs (2415 and
2445). The collaboration session then returns to the CSA in order
to display results. After a collaboration sessions initial
feedback, the buyer can proceed to the pre-negotiation session.
[0256] Finally, after an initial search but before a negotiation,
at the pre-negotiation (2450) level, collaboration can occur
between a B-IA (2455) and at least two sellers (2465 and 2480) in
order to specify comparable item parameters. Once an item
specification has been clarified via collaboration at the
pre-negotiation level, the INAs proceed to the negotiation sessions
(2470). Data is preserved for all collaboration sessions and saved
by AAs (2475 and 2500) for future access.
[0257] FIG. 24 shows the collaboration process for made-to-order
(MTO) customization. A B-IA (2509), after being informed by a B-AA
(2499), interacts with at least two S-IAs. The S-IAs (2503, 2504
and 2505), after being informed by S-AAs (2495, 2496 and 2497)
(which interact directly with corporate databases (2490, 2491 and
2492)), interact with the B-IA (2501. Following an agreement over
item specifications, the S-IAs download the specific information
about comparable items into a specific part of the showcases (2515,
2516 and 2517). The CSA (2511) then accesses the showcases in order
to get initial data on item price, attributes and terms before
proceeding to negotiation sessions. Data that is exchanged between
a B-IA and S-IAs is typically time-sensitive. That is, the
agreement reached between buyer and each seller regarding item
specification usually only holds for a limited time. This is
because the sellers circumstances involving a customized order may
change and thus the details of agreement on the item cannot hold
for more than a reasonable amount of time.
[0258] In an additional embodiment, collaboration can occur during
a search. In another embodiment, collaboration can occur after a
search but before a pre-negotiation session.
INAs
[0259] Intelligent negotiation agents (INAs) are complex autonomous
software agents programmed to conduct interactive negotiations with
specific rules or goals. Because INAs are intelligent--they use
artificial intelligence technologies--they evolve their operations
beyond initial pre-programmed parameters so as to adapt to changing
market conditions. In order to develop adaptive programming, the
agents operate within a multi-agent system (MAS) according to
second-order system rules that govern the primary rules of the
immediate negotiation functions. Such MAS meta-rules allow the
agents to evolve operational negotiation rules in a complex
distributed computer system.
[0260] INAs operate in the context of buyer and seller
interactions. INAs have several main types, including seller-INA
(S-INA), buyer-INA (B-INA), dynamic-INA (D-INA) that switches roles
from buyer to seller and vice-versa, and cooperative INA (C-INA)
[buyer C-INA (B-C-INA), seller C-INA (S-C-INA), lead B-C-INA and
neutral C-INA] involved with aggregation and combinatorial
auctions.
[0261] The present invention uses INAs in a distinctive
demand-initiated system in which a B-INA, after performing an
initial query with a CSA and receiving a report of initial bids
from at least two sellers for a comparable item (or items in a
bundle), bids simultaneously with the S-INAs.
[0262] In order to conduct automated negotiation between the B-INA
and at least two S-INAs the interactive negotiation process
operates by the INAs using complex program code that (1) performs
specific negotiation functions using game theoretic processes,
negotiation strategies and dynamic pricing information and (2)
evolves beyond the initial negotiation parameters in order to
conduct multivariate multilateral anticipatory real-time
interactive negotiations with mobility in a distributed computation
system by employing AI technologies.
[0263] The demand-initiated buyer driven negotiation process
operates by each agent (a) receiving, (b) reviewing and (c)
evaluating the input bids and (d) deciding the best options to
respond. While an expert system pre-programmed with negotiation
parameters can perform these operations, much like a sophisticated
chess game, the present system applies AI technologies to adapt the
negotiation operations beyond the initial parameters (within
second-order MAS meta-rules).
[0264] Such AI-applied adaptation beyond initial negotiation
parameters allows a B-INA to assess incoming bids, evaluate the
bids and chose an optimal response. The evaluation process consists
of comparing each respective bid to various scenarios within
programmed parameters. In order to review, assess and evaluate
prospective bids, INAs access AAs (to receive analytical and
synthetic reports) and select an optimal choice of a bid response
among several options. Evaluating two or more S-INA complex bids is
distinctive from interaction with only one S-INA because
competition provides a more complex negotiating configuration. In
addition, such a process anticipates opponents' negotiation
strategies and seeks a prediction scenario that factors into its
counter-bidding programming.
[0265] In reference to FIG. 25, interactions between a B-INA and at
least two S-INAs are illustrated. After an initial search request
(2570) and search response (2580), a session is initiated in which
a B-INA interacts with at least two S-INAs in order to agree to
multi-lateral rules of negotiation (at 2585). Once pre-negotiation
rules are set, the agents proceed to negotiation sessions (2590)
between the B-INA and S-INA1 and between the B-INA and S-INA2. The
locations of the interactive negotiations may be at the buyers, the
seller(s)', or alternate between the two types of locations. At
some point, the buyer selects a winner (2595) and awards a
contract. Interactions between the B-INA and non-selected S-MNAs
automatically terminate after winner determination. The winning
S-INA then passes the transaction terms to the winning sellers
S-ITA (2605) and the B-INA passes the deal terms to its B-ITA
(2600). If terms are not verified or certified (i.e. by credit
checks), the transaction is then sent back to negotiation sessions
for re-negotiation of those terms and then returned to the ITAs for
clearance. Once the ITAs clear the transaction, the negotiation
ceases (2610) and the session ends.
[0266] In the preferred embodiment, at least four sellers will be
ranked at the first search response. See FIG. 36. The (at least)
four S-INAs will interact in the pre-negotiation session with the
B-INA. The negotiation session allows the B-INA to select two
S-INAs on which to focus further negotiations (suspending, or
stopping the others). FIG. 25 emphasizes the negotiation process
during the phases of negotiation once two finalist S-INAs have been
selected for negotiation by the B-INA. Consequently, the winner is
determined as referenced in the paragraph above. However, in the
preferred embodiment, illustrated in FIG. 29, a tournament
configuration is present in which "contestants" are inevitably
eliminated until a single winner is determined. In another
embodiment, more than one seller can be selected for a
transaction.
[0267] Because, logistically, if B-INAs negotiate simultaneously
with at least two S-INAs, and if the locations are different
between the S-INAs, the B-INA may be in at least two different
places at the same time. There are two solutions to this problem
that are both employed. First, the B-INA may alternate functions by
rapidly moving from location to location, though this solution
leaves the necessary problem of time-delay lags in activity. The
other solution involves the launch of B-INA micro-agents that
simultaneously interact in different locations and constantly
update each other. FIG. 72 shows micro-agents.
[0268] The negotiation process between a B-INA and S-INAs may
include counter- bidding processes directly between the B-INA and
specific interactive S-INAs. FIG. 26 illustrates a final stage of
negotiation between at least two S-INAs and a B-INA. Without
showing the pre-negotiation phase, or inter-agent activity, this
figure describes the counter-offer process, at 2685 and 2695
between the B-INA and S-INA1 and at 2705 and 2710 between the B-INA
and S-INA2.
[0269] In the preferred embodiment, the interactive counter bidding
process may continue between buyer and seller INAs for multiple
sessions. Since counter-bidding is based on factors beyond price
alone, the potential criteria are compounded in complexity.
Consequently, the process of negotiation with each seller may be
protracted. In addition, since there are at least two sellers, a
competition between the two bidders creates a sustained process of
bidding not duplicated in single bidder type negotiation sessions.
For instance, the competitive frontier of a negotiation is more
likely to be extended and optimized with two or more seller
bidders. The buyer has the option of disclosing all or part of the
negotiation sessions with other S-INAs and such disclosures can act
to increase competitive bidding.
[0270] Though, in FIG. 26, only two sellers are specified, several
sellers can be negotiated with (simultaneously) by a B-INA, thereby
increasing the complexity of negotiations. If several sellers are
negotiated with, a B-INA may elect to either negotiate
simultaneously with several S-INAs, or may prefer to narrow the
field, as illustrated in FIG. 26, to two competitors, before
selecting a final winner. After a winner is determined, sending
messages to the remaining negotiators terminates negotiations with
other S-INAs.
[0271] Referring to FIG. 27, the pre-negotiation process is
described. After a customer requests negotiation terms (2755)
through a B-IA (2760), the customer proceeds to a B-INA (2765).
Several sellers, shown here at 2770, 2775, and 2780, are selected
and meet the B-INA at a pre-negotiation session (2785) to determine
interactive negotiation parameters. These parameter factors include
locations, protocols, auction methods, etc. If agreement cannot be
reached with a seller, the B-INA may proceed to negotiate with
another seller. If at least two sellers can agree with the B-INA
about preliminary protocols, communication aspects and other
meta-issues, the B-INA and S-INAs proceed to establish (at 2790)
rules regarding negotiation sessions, the range of the number of
sessions and other parameters. Only when a B-INA can agree
separately with at least two S-INAs can the agents proceed to the
negotiation sessions for multi-lateral one to one interaction. The
locations of pre-negotiation may be the buyer, the various sellers,
or alternating between buyer and sellers.
[0272] One of the ways to conceal agent negotiation strategies, as
illustrated in FIG. 28, is to use time based modulation to disguise
agent interest. Negotiation responses can be quick or slow
depending on the intentions of the agents. Specifically, providing
contradictory actions can conceal agent intentions. Such disguised
actions provide signals that are difficult for opposing parties to
read. One way to accomplish this is for INAs to employ a randomizer
that can alter the composition of the content of a bid so as to
deceive an opponents anticipation of moves.
[0273] In reference to FIG. 29, INA logistics are described. After
initiating the session (2690), agents are generated and identified
by codes (2695). The initial agent interaction protocols are
generated (2970) in order for the agents to establish a common
communication methodology. Such communication processes involves
translation (2975) and synchronization (2980). Failure to
synchronize communication leads to a termination at 2995. Once
fully synchronized, INAs may construct unique negotiation
strategies using AI (2990) through an AAs information (2985). At
this point, agents signal the intention (at 3000) to negotiate with
other agents. After signaling to other agents, INAs send out
communication streams (3005) to their home base, thereby constantly
revealing to the home base their locations, status and plans as
well as receiving periodic parameter modification updates from
home.
[0274] At 3010, a B-INA and S-INAs enter pre-negotiation sessions
to set rules for further negotiations. Information about these
pre-negotiation sessions is sent home (back to 3005). After
pre-negotiation, a B-INA launches micro-agents (3015) in order to
negotiate simultaneously with S-INAs at different locations. At
3020, INAs enter the negotiation sessions, which can lead to
agreement between a B-INA and a S-INA (at 3024) and a provisional
transaction completion (3025). It can also lead to INAs' ceasing
negotiation (at 3023) in which case INA settings are saved (3030)
for later re-launch. Once a transaction is provisionally completed
by the acceptance of an S-INA by a B-INA, the S-ITA (3035) and
B-ITA (3040) activate. Either ITA may return the transaction back
to negotiations, or if both approve it, the transaction may close
(3050) and agents self-terminate (3045) by saving INA settings for
later re-launch (3030), and the session closes.
[0275] In FIGS. 30 and 31, INA interaction sequences are described.
In FIG. 30, after initiating an initial commercial search request
(3075) by a CSA, agents pre-negotiate at 3080. Those that do not
successfully complete pre-negotiation return to 3075. Upon
pre-negotiation completion, agents initiate negotiation sessions at
3082. Upon initiation of negotiation sequences, agents activate
specific negotiation strategy and tactical modules (3085). While
negotiating at different locations, buyers and sellers involved in
the negotiations track mobile agents (3090) and continuously
register the interaction activity with their home bases. Once a
winner is determined (3095) by a buyer selecting a seller,
negotiations between a buyer and seller lead to an initial
commitment (3100). A buyer or seller can withdraw from the initial
agreement (3105) by exercising a contingency and return to an
initial CSA request. The successful INAs can push the transaction
to the ITAs (3110); if the transaction is not completed by either a
B-ITA or S-ITA, it is returned to negotiation sessions (3082).
After the deal is finally closed, the settings are saved (3115),
the agents self-disable, and the session closes.
[0276] In FIG. 31, INAs access AAs (3125 and 3130), which are both
endowed by AI (3122), and then enter pre-negotiations (3135).
S-INAs access the pricing module (3145), negotiation strategy and
tactical modules (3155), and the auction module (3160) before
proceeding to the negotiation session(s) (3165). B-INAs access the
negotiation module (3155) and auction modules (3160) before
proceeding to negotiation session(s) (3165). After negotiation is
completed, the transaction continues to the ITAs (3170) and then
either back to negotiations, or to close (3175). In order to get
more access to the pricing, negotiation and auction modules, the
transaction can return from the negotiation session(s) to the
pre-negotiation session(s) stimulated by either the buyer, or the
seller agents.
[0277] Both referring to INA system architecture, FIG. 32 describes
the early interactions and FIG. 33 emphasizes the sequencing of INA
negotiation sessions.
[0278] Referring to FIG. 32, three showcases are highlighted. Each
showcase receives inputs from S-IAs, S-AAs and corporate databases.
Promotions and RMOs also interact with showcases (and B-INAs).
After the CSA (3270) accesses the showcases with an initial search
request (3280) and results displays results (3282), a B-INA (3285)
reviews the data with the help of a B-AA (3290).
[0279] Note that the B-INA proceeds at 3300 to the pre-negotiation
sessions(s) with only two of the S-INAs (3295 and 3305). In the
embodiment illustrated here, the field has narrowed from three to
two. In the preferred embodiment, four or more showcases can be
accessed and at least four S-INAs can be involved in
pre-negotiations and in negotiations, with a narrowing of the field
from at least four to at least two until a winner S-INA is selected
(at 3315) by a B-INA.
[0280] In FIG. 33, negotiation sessions are illustrated. In session
one (3440) at least the three sellers are specified, but in session
two (3443) negotiation occurs only between the B-INA and S-INA 1,
on the one hand, and the B-INA and S-INA 3, on the other. In
negotiation session three (3445), the B-INA focuses only on S-INA
3. After terms are negotiated and agreed to, the B-INA selects a
winner and either proceeds to ITAs for completion or returns to
3440 for negotiation with several S-INAs. These negotiations may
occur at various, or even alternating, locations. Each negotiating
session can continue for numerous interaction sequences and may
include criteria beyond price alone. The negotiation sessions may
occur in sequences that narrow the field, as described in FIGS. 32
and 33, or may occur simultaneously until a B-INA selects a
winner.
[0281] FIG. 34 shows the time-based sequences of negotiation
session(s). At 3505, a first search request leads to a first seller
ask (3510) (or the first request information display) and to the
first buyer offer (3515). The first seller counter offer occurs at
3520 followed by a second buyer counter offer (3525) and second
seller counter offer (3530). In this illustration, the buyer may
accept the second seller counter offer at 3535.
[0282] Referring to FIG. 35, pre-negotiation session(s) (3570)
determine the buyer (3575) and seller (3580) locations at which to
conduct negotiation activities. The B-INA can negotiate at its home
or the sellers' home(s) or can alternate between locations at
various times during the negotiation process.
[0283] FIG. 36 shows the narrowing process of INA winner
determination in a tournament configuration. In this figure, four
S-INAs are accessed at 3625, during the first phase, by the B-INA.
Two S-INAs (3630 and 3635) are then selected at the second phase by
the B-INA (3640). In the third session, at 3645, an S-INA is
determined by the B-INA to be the winner.
[0284] One of the key innovations of the present system is the
ability of agents to negotiate on factors beyond price alone. These
negotiable variables include item quality, item features, item
quantities, terms of item finance and delivery, and other
terms.
[0285] Referring to FIG. 37, multivariate negotiation is described.
After an initial CSA search request (3705), two S-INAs provide
first "ask" information (3710 and 3715). This first ask can be in
the form of a price or, additionally, of a range of information
about item features and qualities. The existence of item
information beyond price alone suggests that the initial search
request is substantially more than a mere RFQ, which focuses only
on price. Such broader search request and response is also more
conducive to custom orders. At 3730 the buyer provides a first
counter-bid to each S-INA initial ask; each counter-bid can
reference item features, quality, etc. as well as price.
[0286] At 3735 and 3740, the S-INAs provide their respective
counter offers to the buyer first counter bid. Each S-INA has
access to customer and market data, supplied by S-AAs at 3720 and
3725, respectively. However, each S-AA may supply or emphasize
different kinds of data, which may influence the S-INAs first
counter bid. This information input may involve collaboration so as
to narrow the focus of customer data in order to facilitate
customization. Given different information emphasis and the various
item factors, each S-INA may provide quite different counter offer
responses.
[0287] The B-INA (and its micro-agents) may provide second counter
bids (3745 and 3750) to the S-INA counter offers. Again, the S-INAs
respond with second counter offers (3755 and 3765). This process of
counter-bidding and counter-offering may continue for numerous
sessions, either with multiple sellers, two sellers or only one
seller. The S-INAs may terminate the bidding/offering process at
any time.
[0288] In the present example, the B-INA continues to focus on the
negotiation process with S-INA 2 by accepting the offer at 3775.
The contact is then sent to ITAs (3770 and 3780) for re-negotiation
of some points or to final closure of the transaction, thus ending
the negotiation session.
[0289] Various factors--such as item quality and features or
transaction terms-beyond price alone can be negotiated in these
sequences. A buyer or seller may accept transaction terms before
proceeding to two or more counter-offers or the participants may
negotiate for thousands of interactions until agreement is reached
on all aspects of the transaction. Finally, the sequences can occur
interactively between only one B-INA and one S-INA or between on
B-INA and multiple S-INAs. This complex, multi-lateral, interactive
negotiation process creates very dynamic scenarios, like occurring
in one or multiple sessions.
[0290] Automated negotiation is illustrated in FIG. 38 in a
demand-initiated sales system. The sequences specified alternate
between buyer and seller in a compromise process within
pre-established parameters between one buyer (B-INA) and one seller
(S-INA). In the illustrated example, there are two main parts of
the process. The first part of the process negotiates a first
variable, while the second part negotiates a second variable. In
the current example, the seller provides a first price (3770),
which is countered by a buyer (3772). The negotiation proceeds to a
final compromise price (3786).
[0291] The second set of variables is similarly negotiation by
pre-established parameters until a final compromise is reached
(3806). The outcome of the second variable(s) negotiation may
influence the first variables negotiation outcome, and thus the
first variable may require negotiation. Once equilibrium is
achieved in the numerous variables, the negotiation process is
completed.
[0292] In an additional embodiment, the negotiation between a B-INA
and at least two S-INAs shows the complex dynamics of automated
negotiation dynamics over multiple variables. In another
embodiment, this multi-lateral multivariate automated negotiation
process occurs with mobility in alternating locations with INAs
moving program code as illustrated in FIGS. 39A and 39B.
[0293] FIGS. 39A and 39B illustrate the negotiation process in a
distributed system with mobility between a buyer and seller. The
present example focuses on a one-to-one negotiation between a B-INA
and a S-INA. After a buyer (B-INA) initiates a negotiation session
with a seller (S-INA) (3820), the INAs identify possible locations
(3822) and specify agreed locations (3825) at which to negotiate.
In the illustrated example, the B-INA moves to the S-INA location
with program code (3827). The S-INA identifies incoming B-INA entry
after activation and security protocol approval (3830) at the S-INA
location.
[0294] The agents engage in (3832) and complete (3835) negotiation
tasks, after which the B-INA "phones home" by notifying the buyer
"home" computer of remote location activities by sending a message
(3840). After reviewing more tasks at the remote S-INA location,
the B-INA (3845) either terminates (or returns home) (3850) or
assesses additional tasks using internal database and analysis
(3855), assess (3857) and identifies (3860) the next location for
task execution and moves to another locations (3865).
[0295] After moving its program code (3870), the B-INA identifies a
need for AI computation (3875), requests AI computation resources
at a specified location (3880), identifies available AI computation
resources (3885) and messages a request for AI computation
resources to be sent to a specific location (3890). The B-INA
receives (3895) and tests (3900) the AI computation resources at a
specific negotiation site (3895). The negotiations are completed at
the remote location (3905) and the B-INA returns home (3910).
[0296] In an additional embodiment, the B-INA sends its "children"
or micro-agents (cf. FIG. 72) to remote locations because it must
be split into at least two parts in order to negotiate
simultaneously with two S-INAs.
[0297] In FIGS. 39A and 39B, though a one-to-one interactive
negotiation is shown between one B-INA and one S-INA, a B-INA (or
its micro-agents) may negotiate simultaneously with at least two
S-INAs at two or more S-INA locations (as well as buyer or
intermediary locations) in an additional embodiment. The B-INA and
S-INAs may also alternate between the various locations according
to the agreed negotiation requirements of the INAs.
[0298] Not only are negotiations multivariate and interactive, but
they are also multi-lateral. FIG. 40 illustrates how a B-INA can
simultaneously negotiate with several S-INAs. A double shout
auction embodies an interactive process between buyer and seller.
In our example, a double shout auction can occur between a buyer
and multiple sellers. Each negotiation process is two-way and
allows multiple sessions. Ultimately, the multi-lateral approach
will narrow the field as specific seller competitors drop out of
the negotiation process after the selection of the winning
seller.
[0299] FIG. 41 refers to the INA negotiation module and a
negotiation method schema. The list of negotiation strategies and
methods refers to specific approaches and techniques that INAs may
employ to automate negotiations in specific situations.
[0300] In one-to-one interactive negotiations, negotiations between
a B-INA and an S-INA may be cooperative or competitive. If
cooperative, the negotiation sessions can use either an exchange
based approach or a problem-solving approach, as described. If
competitive, the negotiation sessions are dialectical or
oppositional, or deterrence based. Unlike other approaches, a
deterrence negotiation approach uses a non-zero-sum game.
Negotiations may also be buyer-initiated or seller-initiated.
[0301] In multi-lateral negotiations, either one buyer can
negotiate with multiple sellers, or several buyers can negotiate
with multiple sellers. Whether one-to-one or multi-lateral,
negotiations can cover different terms and goals of each party, as
well as multiple item packages.
[0302] Automated negotiation can occur by establishing pre-set
expert-system strategies in a game theoretic environment with
specified constraints (i.e., time, information or choice). However,
by applying AI technologies, automated negotiations can be adaptive
to constantly changing conditions. Such adaptation involves the
anticipation of opponent potential activities, as well as of
changing situations.
[0303] The addition of mobility creates another layer of
complication for automated negotiation because location changes add
logistical and sequential issues in the mechanics of negotiation
operations. The use of AI and mobility make demand-initiated
automated negotiation processes increasingly dynamic.
[0304] FIG. 42 refers to the INA auction module, which specifies
auction types. These auction categories may be employed by INAs in
negotiation sessions. The auction types, either alone, or in
combination (or alternating sequence), can be mutually selected by
buyer and seller(s) during the pre-negotiation stage of the
negotiation process. Though they appear to be generally biased
towards the seller side, these main auction types are all
interactive, and may be used in conjunction with complementary
auction types. For instance, an ascending auction when combined
with a descending auction in an interactive environment, leads to a
double shout auction. A Vickrey auction merely modifies an English
auction. A Vickrey auction may alternate with an English auction as
part of the overall "discount" method proposed by a seller to give
it an advantage.
[0305] The negotiation module relies on information and analysis
from the auction module and, in the case of S-INAs, the pricing
module. FIG. 43 illustrates these interactive relations between the
modules. The negotiation module also accesses AI when necessary.
Once negotiation methods, strategies and tactics are selected by
INAs, the INAs proceed to INA sessions (4112, 4117 and 4125). via
the AAs. Both B-AAs and S-AAs interact with the negotiation
module.
[0306] Referring to FIG. 44, the pricing strategies module is shown
in relation to AA interactions. Market data (4155) informs
competitor prices (4160) and the pricing strategies module (4175).
Both B-AAs (4163) and S-AAs (4165) have data entered from the
pricing module as well as by AI (4170). The process continues to
the INA sessions (4180).
[0307] INAs, whether seller or buyer, do not need to have purely
neutral stances from which to act. In fact, INAs may have
personalities or attitudes. FIG. 45 refers to examples of
personality traits that INAs may have as well as the dynamics of
INA interaction. Depending on supply and demand imbalances, INAs
may be optimistic, opportunistic, and aggressive if sellers have
shortages, or if buyers have surpluses.
[0308] In order to disguise INA activities, INA personalities or
attitudes may vary and alternate between the optimistic and the
pessimistic, or between the opportunistic and the
un-aggressive.
[0309] In an additional embodiment, ITA functions can be included
in an INA for concurrent program execution. In another embodiment,
AA functions may be included in an INA. These embodiments may
include abbreviated versions of agents for enhanced efficiency of
program code operation. Finally, because they are autonomous, INAs
use intelligence. The use and implications of applying AI to INAs
provides an important layer of mobility which represents an
additional embodiment.
C-INAs and Aggregation Methods
[0310] Referring to FIG. 46, cooperative INA (C-INA) (4530)
intermediation allows a neutral agency capacity by brokering
negotiations between B-C-INAs and S-INAs. A C-INA (4530), after
accessing a CSA (4520) (which accesses various showcases and
presents a report of an initial query), acts as a broker between
S-INAs (4525) and multiple buyer C-INAs (4535). Because the
B-C-INAs congregate for the pooling process and because they
cooperate for the purposes of aggregating for better discounts,
specific items from S-INAs may fill specific (multi-item) baskets
of B-C-INAs at specified intervals by using ITAs (4540).
[0311] Since B-C-INAs can be essentially B-INAs that aggregate or
work together in order to cooperate for discounts and more
substantial buying power than individual B-INAs, there are several
sources that initiate B-C-INA transactions. FIG. 47 describes these
B-C-INA transaction initiation sources. In all cases, promotions
from sellers are provided to, or invited by, sellers. However, in
one embodiment, a B-C-INA may identify an opportunity that may
require group buying power and inform other B-C-INAs so as to pool
a cooperative group for this opportunity.
[0312] FIG. 48 illustrates B-C-INA aggregation. After accessing
showcases and promotions, a CSA (4625) makes search requests for a
specific item or multiple items (packages). The lead B-C-INA (4635)
interacts with other B-C-INAs so as to coordinate and prioritize
their preferences. At least two S-INAs (4850 and 4655) interact
with B-C-INAs. The lead B-C-INA may act as a sort of consolidator
in this context, in effect providing initiation and clearinghouse
agency functions. Buyer IAs and pre-negotiation stages are used
here in the preferred embodiment similar to ordinary B-INA and
S-INA interaction early stage negotiation processes.
[0313] As demonstrated in FIG. 48, B-C-INAs can initiate
coordination when they realize common interests and communicate
with each other. In general, from the seller viewpoint, this form
of simple aggregation is merely a method to sell a quantity of
items to multiple buyers. Upon realization of common interests,
B-C-INAs may be simultaneously coordinated for group buying
opportunities. Any B-B-INA can broadcast opportunities to other
B-C-INAs with similar interests. These broadcasts are sent to
B-C-INAs through registries that identify and inform similarly
interested parties. The B-C-INA that broadcasts a buying
opportunity then leads the aggregation process for its follower
B-C-INAs. For the purposes of the aggregation process, B-C-INAs use
tags to track their movements in the congregation process that
precedes aggregation. In the current system, a competition between
at least two S-INAs over comparable items ensures a competitive
environment which provides greater value to B-C-INAs.
[0314] A seller may trigger buyer cooperation by initiating a
promotion on items or packages focused on groups of buyers.
B-C-INAs may, in the course of negotiation with S-INAs, compromise
in order to agree to the simplest items on their agenda, eventually
filtering out the less common, mutually interested items. In this
way, agreement between multiple buyers may be more easily and
quickly reached.
[0315] In addition, as illustrated in FIG. 52,customization can
occur with this general aggregation method because specific items
may vary in feature choices for maximum item differentiation and
customer satisfaction: Made-to-order (MTO) B-C-INA congregation is
facilitated in this way
[0316] After the buyers and sellers complete the negotiations, they
proceed to S-ITAs (4660 and 4665) and B-ITAs (4630), where upon
winner S-INAs are determined and items are allocated. ITAs--whether
buyer or seller--may require the completion of more tasks, in order
to close the transaction (4675).
[0317] Aggregation, in general, is a method to sell items, or
bundles of items, to multiple buyers. Automated aggregation, in the
context of the present system, may have several forms, including
those shown in FIG. 49. The bundles can be multiple quantities of
identical (or near identical) items as well as pre-set and open
bundles. Pre-set bundles are specified combinations, while open
bundles are any combination of items. The distinctive distribution
patterns of the various types of aggregation--listed in 4740 thru
4765--each refers to a unique approach.
[0318] FIG. 50 refers to the mass pooling method of automated
aggregation using INAs with multiple buyers and multiple sellers.
B-C-INAs (4805, 4810, 4815, and 4820) pool their common interests
by working together to procure specific items or bundles of items
within pre-determined time constraints (4825). As time deadlines
pass, specific item sets are distributed from sellers to interested
buyers (4830); such common bids and negotiations between buyers for
seller items are distributed by sellers at various locations
(4835). B-ITAs and S-ITAs process the orders or require further
negotiation (4840 and 4865). Once approved by both sets of ITAs,
final orders are distributed to buyers (4870) and the session(s)
are closed after a quorum of items bought has been satisfied. In
this way, multiple sellers sell to multiple buyers once specific
constraints have been satisfied over time.
[0319] FIG. 51 illustrates the disintermediated method of
aggregation employed by the present system prior to the negotiation
phases. In this example, several seller INAs (5005, 5010, and 5015)
sell specific items (1-6) and preset bundles (i.e., specific
combinations of items) (1-3). The S-INAs use S-AAs (5017) that use
forecasting analysis of item combinations (5016) and demand shaping
of time-sensitive promotional invitations (5020) (e.g., if a
surplus of items creates an incentive by sellers to shift buyer
demand from scarce items). S-AAs inform promotions (5025) which are
provided to the lead B-C-INA (5030). The lead B-C-INA then selects
items (5035) that follower B-C-INAs (5045) may be interested in.
The B-C-INAs then enter into negotiations with the S-INAs for
specific items and bundles (5055).
[0320] Observe that aggregation distribution occurs in this model
with overlapping item demand. Similar item categories can be custom
configured with specific features for particular customer needs
while also providing aggregation capabilities. An example of this
might be customers ordering ten thousand pairs of blue jeans, but
with varying exact sizes. This aggregation method allows various
buyers to share a much larger order that may be tailored to its
needs.
[0321] FIG. 52 refers to the aggregation process that provides
disintermediated mass customization. Various S-INAs (5105, 5110,
5115, and 5120) have items 1-8 and pre-set bundles 1-4. The
specific items are ultimately distributed, according to the example
shown in this figure, in such a way that: (1) B-C-INA 1 receives
only item 1, but with features 2 & 3; (2) BC-INA 2 receives
item 1 with features 1 & 4 as well as item 4 with features 2
& 3; (3) B-C-INA 3 receives item 1 with features 1 & 4,
item 4 with features 2 & 3 and bundle 3, and; (4) B-C-INA 4
with bundle 3.
[0322] Because this aggregation process is not performed by
employing intermediated techniques, this automated process,
precisely by using INAs, is disintermediated. Though FIG. 52 shows
the outcome, the INA negotiation process is employed as well as the
main aggregation process using B-C-INAs.
[0323] It is primarily in the context of aggregation employing
C-INAs that the demand-initiated sales process can be reversed. In
particular, for unique items, a single seller may sell to two or
more buyers. This seller demand-initiated sales process represents
an additional embodiment of the present system.
D-INAs
[0324] Referring to FIG. 53, dynamic INA (D-INA) double agents are
described with an emphasis on their arbitrage application. D-INAs
shift roles alternating between buyer and seller. Such a role
change in a sales system can effectively replace the wholesale
intermediary layer. After a CSA (5230) accesses showcases 1-4 and
proceeds to pre-negotiation (5235), a D-INA, in a buyer mode,
enters a negotiation session (5250) with at least two S-INAs. ITAs
close the transaction after the negotiation session with the D-INA
receiving (rights to) the item(s) (5270).
[0325] In the second phase of this illustrated embodiment, the
D-INA switches roles (5265) and shifts to its seller mode (5275)
moving to negotiate with a B-INA along with at least one other
S-INA. After the buyer INA selects an item from a D-INA (now a
seller) and after the ITAs resolve the closing of the transaction,
in this illustration, the item can be provided to a buyer directly
from the original seller (5295) thereby decreasing supply chain
friction and duplication. The transaction is then closed
(5300).
[0326] The use of arbitrage involves the exploitation of limited
buyer information from D-INA intermediaries. One advantage of
arbitrage approaches is the use of information at one location to
exploit at a different location. The present system--which uses
mobile D-INAs in one embodiment--is particularly well suited to
arbitrage approaches in geographically transcendent environments
using D-INAs in their buyer and seller modes.
[0327] In an additional embodiment, D-INAs use RMOs so as to limit
risk. Precisely because there are enhanced risks in arbitrage
situations between a seller and buyer function, RMOs in this
context are compounded.
[0328] FIG. 63 refers to a method for disintermediated arbitrage of
multi-item bundles from multiple sellers to multiple buyers using
D-INAs. Several S-C-INAs (6605, 6610, 6615, and 6620) provide
multiple items for sale and cooperate in order to calculate
multiple buyer bundle bids. In this illustration, at least two
D-INAs (6630 and 6635), after interacting with B-AAs (6625 and
6640), respectively request an initial search for multiple items
via CSAs (6642 and 6414), and then enter into negotiations with the
S-C-INAs (6630 and 6650). Bids are evaluated at 6655 using either
relationship management (6660) or revenue maximization (6665)
strategies, after which the D-INAs select sellers' specific bundles
(6670).
[0329] Once a seller bundle or combination of seller items are
selected by D-INAs (as buyers), the D-INAs change their mode to
that of a seller (6675 and 6680). Using the methods discussed
earlier, the D-INAs then negotiates (as a seller) with multiple
B-INAs for multiple items. In this example, several B-INAs are
winnowed in succeeding phases until the final B-INAs are selected.
The application of D-INAs for multi-items in the seller mode
reveals a dis-aggregation function by selling to several
buyers.
Disintermediated Multi-Item Combinatorial Auctions Using INAs
[0330] In reference to FIG. 54, a traditional, intermediated,
combinatorial auction is illustrated with an application to a
single seller providing items to multiple sellers. In a single-bid
phase auction process, a seller (5505) provides multiple items, 1-5
(5510-5530), via an intermediary (5535), to various B-INAs. The
items are distributed in this example, according to specific
combinations of items, to specific B-INAs. In this example B-INA 1
receives items 2 & 4, B-INA 2 receives items 1, 2 & 3,
etc.
[0331] Referring to FIG. 55, the intermediary is removed from the
transaction in which a seller provides four items to four separate
B-INAs in specific configurations. Buyer A receives items 1 &
3, buyer B items 1 & 2, buyer C items 2 & 4, and buyer D
items 2, 3, and 4. Because no intermediary is involved, a double
opposing shout auction--in which the package price descends for
seller(s) while it simultaneously rises for the buyer--is used
between a single seller and several buyers simultaneously.
[0332] FIG. 56 illustrates a multilateral opposing shout auction in
which items are sold between a buyer and at least two sellers,
either with or without an intermediary. From the viewpoint of a
seller, prices decline, while from the viewpoint of the buyer,
prices increase.
[0333] Factor filters are methods by which to prioritize multi-item
bundles by composition and structure. By distinguishing between
kinds of bundles, negotiation for multiple items between buyer(s)
and seller(s) can be more organized and efficient. Such factor
filtering processes can be applied to combinatorial auctions
employing INAs.
[0334] In reference to FIG. 57, factor filters operate as pruning
techniques (5815) in the process of evaluating multiple bidders
(5810) by either S-INAs or B-INAs (5805). After establishing a
priority preference (5820), several main kinds of bundles--preset
(5825), specific (5830), progressive (5835), quantity (5840),
quality (5845), and temporal adaptive (5850)--are categorized.
Several bundle categories are further sub-categorized as (1)
threshold factor specific (5855), i.e., an item that is critical to
a bundle; (2) need specific (5900), in which complementary item(s)
are necessary in order to make the whole bundle desirable; (3) item
preference (5890), in which a preferred item in a bundle is sought
[A D-INA may buy a bundle for a specific item in order to split the
bundle and resell the various valuable and common parts.]; (4)
successor contingent bundles (5860) in which a first priority item
is sought and only if not acquired then a second priority item is
sought and so on; (5) priority contingent bundles (5865), in which
a first item is sought and, only if the first item is acquired,
will a second item be acquired and so on; (6) quantity bundles
(5895) in which multiple substitutable items are acquired, for
example by more than one buyer as an aggregate; (7) quality
bundles, in which the best items are sought (5867) and for the best
value (5883); (8) dynamic pricing contingent bundles, in which
multiple items depend on time or price priorities across the
product or service cycle (5870) in such a way that value
fluctuations determine item priorities.
[0335] Referring to FIG. 58, disintermediated multi-item bidding
from only one seller to multiple sellers is described. After
several B-INAs receive data analysis from B-AAs, the B-INAs enter
into mutually agreed rules of negotiation (5995) similar to a
pre-negotiation. A seller INA negotiates with the B-INAs for
multiple items (6000) by proceeding to apply factor filters (6005)
in order to establish buyers' specific priorities. Buyers (6007)
select unique sets of specific items in order for the seller to
evaluate the initial bids (6015) for optimal seller benefit.
[0336] In order to evaluate buyer bids, the seller uses two main
approaches. On the one hand, it can use a strategy of short-term
revenue maximization (6020) in which it accepts the overall two
highest bids (6040) for a specific package. On the other hand, it
can use a strategy of long-term relationship management (6010) in
which two winners are selected by using factors beyond price alone.
In an additional embodiment, it can select two winners by
alternating between the two methods.
[0337] Once the two winning buyers are determined (6030 and 6035)
for multiple items, the process enters a new phase. A second phase
of bids are evaluated (6045) and the highest overall bid on
multiple items (6050) by a buyer is evaluated by the seller. A
winner is determined by the S-INA (6055) and the ITAs close the
transaction (6057).
[0338] In an additional embodiment, once the highest overall bids
are determined (6050), and the winner is determined (6055), the
second highest bidder can capture remaining seller items not
included in the first winner package and hence constitute another
package of items. The second highest bidder then becomes the winner
of a second priority bundle of items remaining from the first
bundle of buyers.
[0339] FIG. 59 describes a method for transactions involving
multi-item bidding with multiple sellers to a single buyer using
S-INAs. After receiving inputs from a B-AA (6105), a B-INA (6110)
requests bids from sellers for specific bundles of items. Several
S-INAs work together (i.e., cooperate to mutually agree on
negotiation rules (6115) similar to a pre-negotiation session.). At
6140, bidding occurs by the S-C-INAs to supply packages of items
from various sellers. Pre-set bundles (6150) are bid on and a
winner determined by the buyer (6160).
[0340] However, specific open bundles are bid for at 6140. Pruning
techniques (6155) that eliminate less preferred items and factor
filters (6170) are applied in order to limit bundle composition so
as to increase efficiency. At 6175 seller bids are evaluated by the
buyer. The buyer can use a short-term revenue maximization (6180)
strategy or a long-term relationship management (6165) strategy of
preliminary winner determination. In this example, the former
strategy leads to S-INA 4 being selected and the latter strategy
leads to S-INA 2 being selected. In a second phase of winner
determination, at 6195, a final winner--S-INA 2 (6210)--is
selected. A B-ITA resolves any transaction clearing issues (6205)
and either renegotiates or closes the transaction (6200)
[0341] FIG. 60 shows a process for disintermediated aggregation of
pre-set bundles with multiple sellers and multiple buyers. Various
B-C-INAs (6265, 6280, 6295, and 6300) congregate in order to share
bidding for specific pre-set bundles. Pre-set bundle 1 (6260) is
provided by seller 1 (6255), pre-set bundle 2 (6275) is provided by
seller 2 (6270) and pre-set bundle 3 (6290) is provided by seller 3
(6285). Pre-set bundle one consists of products 1 & 2 and
service 1, pre-set bundle two consists of products 3 & 4 and
service 2 and pre-set bundle three consists of products 5 & 6
and service three.
[0342] In this illustration, buyer one (B-C-INA 1) and buyer two
(B-C-INA 2) select pre-set bundle one from seller one. Buyers one,
two, and three also select pre-set bundle two from seller two. All
buyers select pre-set bundle three.
[0343] FIG. 61 shows a disintermediated bidding approach for
multiple items between multiple sellers and multiple buyers. In
this example, sets of combinations of items are matched between
sellers and buyers. Each horizontal row represents the distinct
items offered from one seller. Therefore, row one represents items
1-4 from seller one and so on.
[0344] In this unique configuration, five buyers bid for separate
specific packages (bundles of items) from among the twelve items
offered from the three sellers. Accordingly:
[0345] Buyer Bidder A seeks items 1, 5, & 9
[0346] Buyer Bidder B seeks items 2, 3, 4, 6, 7, & 8
[0347] Buyer Bidder C seeks items 3, 4, 7, 8, & 12
[0348] Buyer Bidder D seeks items 5, 6, 7, 9,10, & 11
[0349] Buyer Bidder E seeks items 9, 10, 11, & 12
[0350] Note that there is overlap between the items that buyers
seek. This overlap implies that the buyers are competing for these
items. Consequently, bids must be evaluated in multiple item
packages for overlapping items. Combinatorial auctions can evaluate
the competitive bids, but information must be shared between
multiple sellers in order to do so because otherwise, only
incomplete information is available on multi-item packages that
include items not offered by some sellers. This problem of the need
for sellers to share information between themselves in order to
adequately calculate multi-item bundles between multiple buyers
leads to the development of S-C-INAs.
[0351] FIG. 62A illustrates C-INAs used on both the buyer side and
the seller side. In this figure, a disintermediated method of
aggregation is described involving multiple item bidding from
multiple sellers and multiple buyers using C-INAs. In phase one,
various sellers (6415, 6435, 6455, and 6470) offer multiple items
for sale (6420, 6440, 6460, and 6475).
[0352] B-C-INA 1 (6430) is the lead C-INA in this example. B-C-INAs
congregate at various locations at the request of the lead B-C-INA.
Once a quorum of B-C-INAs is established, a pre-negotiation phase
will set initial rules. Each B-C-INA seeks different sets of items
from various sellers. In order to bid on a variety of items
requested from multiple buyers offered by multiple sellers, the
sellers must work together. At 6445, the sellers cooperate by
providing pricing information in order to calculate B-C-INA bid
values. Without this cooperation, incomplete information on items
in bid sets not involving only a specific seller will be
indeterminable. Though complex, the goal of sharing pricing
information between sellers regarding buyer bidding is to manage
auction pricing (and other item factor) data in a limited time
frame so as to establish a competitive real-time market.
[0353] If items between sellers are substitutable, then real
competition between sellers can occur even on multiple items within
packages offered by multiple sellers. Winning bids (6480) are
selected by seller calculations of high bids on multiple items.
Alternatively, long-term relationship management criteria may
develop a strategy of different results than strictly revenue
maximization. Because multiple items are selected by multiple
buyers, there is at best a hierarchy of choices for sellers to
maximize the bidding; such choices produce trade-offs of results
between buyers in which only marginal benefits may separate winners
(6490).
[0354] In a further discussion of this process in FIG. 62B, several
sellers (S-C-INA 1-4) are narrowed in several phases into a final
winner (S-C-INA 2). Though sellers share necessary information in
order to calculate bids, they also compete. In the embodiment
illustrated in FIG. 62B, a unique package of items is selected from
S-C-INA 2 (the winner, at 6550) by B-C-INAs that pool their
interests (6510) into multiple item bundles.
[0355] Bids are made and evaluated (6530) by buyers either
cooperatively or competitively; if cooperatively, buyers may divide
items between themselves after sellers have determined the buyer
winners. The group of buyers may also assemble specific subsets of
items comprising specific packages (6455). In this case the buyers
may bid for a specific subsets of items (6575) for distribution
from among participants after the general sale from a seller. This
distribution process is a form of dis-aggregation.
[0356] In an additional embodiment, the selection of successful
B-C-INAs may occur over time by instituting overlapping time frames
for the filling of buyer baskets.
[0357] FIG. 64 refers to various factors that may be negotiated in
multi-item negotiations.
[0358] FIG. 65 refers to a list of alternative multiple feature
factors involving personal computer configurations. A change in a
single feature changes the composition of the package. Each
completed computer represents a multi-item package. If different
sellers provide the pieces of each computer, then a buyer using the
present system can assemble a multi-item bundle.
[0359] FIG. 66 illustrates examples of multi-item bundle
categories.
ITAs
[0360] Intelligent transaction agents (ITAs) are used to close
transactions. ITAs are either seller-side or buyer-side. ITAs
(6815) interact with AAs (6810) and with INAs (6805) as illustrated
in FIG. 67 in the context of the seller role. The ITA closes the
transaction (6830) after it clears the negotiation. The ITA uses a
compliance function, in these cases.
[0361] FIG. 68 shows an S-ITA operation. After an S-INA
provisionally completes negotiation (6850), it requests the S-ITA
to clear the transaction (6855). The S-ITA initiates a review of
transaction terms and item terms (6850), and accesses a financial
database to check the buyers credit (6865). The S-ITA proceeds to
clear the buyers credit (6870 and 6875) or reject it (6885). If
rejected, the transaction is sent back to negotiation (6890). If
approved, the transaction is closed (6880).
[0362] In an additional embodiment, the INA and ITA work together
symbiotically to clear each variable in a sequence of transaction
steps. As the INA requires an ITA function, it will pass this part
of the transaction for clearance while the INA continues to process
the negotiation functions contingent on ITA clearance. The ITA
represents an autonomous sequential clearing mechanism in a close
relationship with the INA operation.
[0363] ITAs provide an important function similar to an accountant
or lending officer. Without clarifying item and transaction terms,
for instance, an agreement is not complete. Once clarified by using
a checklist of operations that pertain to important functions, the
deal can be closed.
[0364] FIG. 69 describes an S-ITA (6940) and a B-ITA (6945)
interacting in a system process in the final negotiation between
one seller and one buyer. As illustrated, the ITAs interact with
their respective AAs (6920 and 6950) and INAs (6925 and 6930) and
with each other. If the respective INA does not satisfy ITA
transaction clause constraints, the transaction is returned to the
INA for renegotiation of its specific parts during a specific phase
of buyer-seller interaction. Once all constraints are satisfied,
the transaction is closed (6955).
[0365] In an additional embodiment, ITAs may perform their
functions at multiple locations, in sequence or alternating at
various locations. This mobility aspect is achieved by using AI
technologies. ITAs also supply services as listed in FIGS. 70 and
71. At each stage of a sequence of INA interactions, the ITA may
offer these services to buyers and sellers.
[0366] By working closely with AAs, ITAs can analyze data crucial
for transaction completion. Consequently, ITAs can involve services
referenced in FIGS. 88-92.
[0367] In one embodiment, ITA functions may be included in INAs for
optimized efficiency and may be executed concurrently.
Micro-Agents
[0368] A B-INA negotiates with at least two S-INAs to conduct
multiple parallel (or sequential) negotiations. One method to do
this, as described in FIG. 72 (and earlier), is for a B-INA to spin
off various "children" or B-INA micro-agents (7110, 7115, and
7120). Each B-INA micro-agent can complete a negotiation session
with one of a series of S-INAs at various locations (7125, 7130 and
7135). Micro-agents can communicate with each other in order to
conduct parallel simultaneous negotiations with multiple S-INAs.
Once each specific negotiation session between a B-INA micro-agent
and a winner S-MNA (7150) is complete because of mutual agreement
(7155), and the transaction completed (7165), the sessions with
unsuccessful S-INAs are closed (7160) and a B-INA micro-agent
terminates. Micro-agents may use applets or aglets in order to
launch, replicate and activate their program code.
[0369] In an additional embodiment, D-INAs, particularly in a buyer
mode, may use micro-agents to conduct its functions.
[0370] In another embodiment, S-INAs may use micro-agents to
conduct its functions. In particular, in aggregation or multi-item
bundle bidding contexts in which sellers may negotiate transactions
with two or more buyers, micro-agents may be applied in a similar
way.
[0371] Micro-agents are particularly appropriate in complex
multi-lateral negotiation activities in which mobility of agents
between multiple locations in a distributed network are involved.
FIGS. 39A and 39B illustrate a mobile negotiation method in a
distributed system that can be applied to micro-agents.
Artificial Intelligence
[0372] Artificial intelligence (AI) applies in several main ways to
the present multi-agent system, including the use of genetic
algorithms, neural networks (and fuzzy logic), genetic programming
and evolutionary computation. These AI functions are applied to the
operations of AAs, INAs, ITAs, and CSAs. FIGS. 73-80 describe the
unique operations of AI and their distinctive applications to
agents in the present system. By providing learning and
intelligence functions to agents, specific agent actions can be
autonomous. Such autonomous agency provides unique interactions
among agent operators that emulates the complexity of markets. The
present system advances considerably the use of AI in multi-agent
computer program commercial systems.
[0373] Referring to FIG. 73, genetic algorithms are applied to the
present multi-agent system. In a quest to identify an optimal
algorithm for a specific computation action or problem, a search
for the best algorithmic solution commences (7215). Based on
available information, a set of candidate solutions is generated
(7225).
[0374] In a distributed communications system, market data inputs
(7210) are filtered (7220). Candidate solutions (7225) are created
from available information and then new generations of candidate
solutions based on multi-factorial mutations (7235) that are
generated by a randomized mutation engine (7228). Mutated candidate
variants are produced using survivor candidate operators that sort
by variables (7240). These maximized and expanded sets of candidate
solutions are evaluated according to efficiency criteria (7245),
which can be used to select the most optimized candidate solutions.
The best algorithmic candidates are matched with market data (7230)
[via pattern matching (7220)] and then new generations of candidate
solutions are created in a loop from 7230 to 7245.
[0375] The best candidate solutions are kept after testing which
are most successful (7255) while the rest are discarded (7250). New
generations of candidate solutions are bred for regeneration,
filtering and selection. The winning algorithm solution is
determined using the most updated criterion (7260). The solution
results are displayed (7270) and ranked (7280). Sub-optimal
solutions (within specified constraints) are returned to generate
additional candidate variants for future comparison, selection and
use. The optimal solution, relative to all available candidates
(7275), is applied to agent analysis or activity (7285) and the
program run is ended (7290).
[0376] Referring to FIG. 74, neural networks are applied to the
present multi-agent system. Initial solutions (7315) to computation
problems are developed based on available information, typically
market data inputs (7305). NNs are generated (7325), in part based
on available market data and in part based on a comparison of
optimal statistical scenarios (7330). Statistical scenario
comparisons may involve fuzzy logic inputs (7320).
[0377] Neural networks are trained (7335) for fitness using
training patterns that run through a process of trial and error
until a specific set of candidate NNs is identified that optimize
computation solutions. These patterns are compared to market data
inputs. NNs are matched for optimal fitness patterns (7340). During
this process of pattern matching, mutations may occur using a
mutation engine (7345) that employs random (7355) and alternating
sequences (7360). Such mutated NNs are retrained using efficient
parallel computation resources. The most fit NNs (7340) are pruned
(7350), tested (7365) and ranked. After ranking each NN generation
for fitness (7370), a stage equilibrium point is reached (7372).
The less fit NNs are retrained and replaced with expanded retrained
NNs. (7375). Finally, a select NN is applied to an agent analysis
or activity (7380) and the computer program run ends.
[0378] In FIG. 75, a genetic programming system process is
described. Data inputs (7505) are applied to rule based learning
(7510), regression analysis (7515) and induction decision trees
(7520). Rule based learning uses a pattern matching pruning
approach (7525) that leads to the development of heuristic
operational rules (7535) that relearn (7545). The heuristic
operational rules, the regression analysis, and the induction
decision trees are applied to organizing models (7530). The
organizing models are measured by scope, accuracy, and errors (and
exceptions and missing values). These operating models present
output scenarios (7550) as statistical positive (7560), or negative
(7565) recommendations or as forecasts (7555). These
recommendations are applied to agent analysis or activity (7570)
and the computer program run is ended (7575).
[0379] GP learning schemas are described in FIG. 76 as a table of
various main learning types.
[0380] FIG. 77 describes evolutionary computation applications to
agents in a multi-agent system. After testing GA approaches for
success, the system proceeds to test GP, and NN approaches. This
filtering approach of testing EC techniques operates like a switch.
The evolutionary computation approaches (7605) of genetic
algorithms (7610), genetic programming (7615) and neural networks
(7620) are applied to the CSA (7625), the AA (7630), the INA
(7635), and the ITA (7637). Each agent uses differentiated
reasoning schemas that are specified. Each agent type also
specifies the advantages of each application (7640, 7645, 7650, and
7655).
[0381] FIG. 78 describes AI applied to agents in a distributed
system. An agent requests (7675) and accesses (7680) AI application
to solve a problem at a particular location. The agent then
identifies optimal AI application by using a filtering process
(illustrated in FIG. 77) that test GA, GP and NN processes for a
specific use (7685). The agent then applies AI efficiently for a
specific use (7695). If an agent requires more AI (7690), it
returns to 7675. If it has sufficient AI to complete a task, the
agent completes the session (7700).
[0382] Referring to FIG. 79, an evolutionary computation
architecture is described with reference to AA and INA
applications. GA (7715), NN (7725), and GP (7720) modules breed
optimal programs (7730) using the efficiency module (7735). These
programs and other evolutionary computation methods (7740), as well
as multiple simultaneous evolutionary computation approaches
(7745), and an expert system (7755) driven inference engine (7760),
create, test and optimize various evolutionary programs (7750).
[0383] These evolutionary programs are applied to B-AAs and S-AAs
(7765) as intelligent analytics for use in specific forecasting
(7780), analysis (7785), synthesis (7805) and collaborative
filtering (7800) functions. The evolutionary programs are also
applied to INAs either through optimal negotiation approaches
(7775) and non-intentional disguised negotiation strategies (7770).
Negotiation approaches use auction typologies (7795) that are then
applied to various INAs (7790).
[0384] AI is applied in distinctive ways to the present system.
Techniques empower agents to be autonomous and, hence, mobile. EC
endowed autonomous intelligent software agency is applicable to
negotiation agents using several specific methods. In the present
demand-initiated system, AI is applied to: (1) the B-INA process of
narrowing from several S-INAs to two S-INAs; (2) the process of a
B-INA automatically negotiating with two S-INAs, (3) the process of
interactive multivariate B-INA and S-INA negotiation and (4) the
process of using mobility in automated demand-initiated
negotiations in a distributed environment. Because the
demand-initiated negotiation contexts of negotiation have a buyer
bias, the notion of AI-driven autonomy is unique. In all cases, the
application of AI to the present system is crucial in order to
allow INAs to adapt to changing circumstances, to anticipate the
changing scenarios and to accommodate decision processes that
emulate human behavior.
[0385] Furthermore, the specified AI approaches are applied to INAs
in the aggregation, arbitrage and combinatorial contexts of a
demand-initiated system precisely because of the immense complexity
of automating these complicated functions. AI-induced INAs can
solve complex negotiation problems within specific rules that
pre-programmed systems cannot. Since the present invention involves
several dimensions of complexity, including multivariate,
multilateral, combinatorial and mobile aspects in a
demand-initiated system--which are exponentially more complex in
combination--AI approaches are increasingly pivotal.
Mobility
[0386] The problem of mobility with intelligent agents is solved by
applying layered AI. FIG. 80 refers to layered AI for optimal agent
mobility. By keeping initial demands for computation to an
efficient minimum, the system resembles a RISC (reduced instruction
set computing) software architecture approach that strongly
benefits the need to keep mobile agent program code as efficient as
possible. At the same time, huge computer resources are accessible
when necessary--either from the users "home" computer or an outside
service provider--in order to provide powerful computation to meet
peak agent needs, especially in time-sensitive or complex
transactions. This approach may resemble a biological immune system
which, when it detects an anomaly, brings to bear a larger arsenal.
In the case of mobile computer devices, which tend to posses
minimal computation resource capacity, the application of layered
AI promotes the use of mobile program code so as to efficiently
enhance scarce resources.
[0387] Though mobile, intelligent agents have a "home" base (7860),
or a computer source location from which it is launched (7870).
After launching, the agent(s) make an initial determination of
minimum expected computer resources required for a specific
activity based on initial (pre-negotiation) interaction (7885).
After the initial determination of efficient computer resources,
the agent(s) enter into interactions (or analysis) using specified
AI function levels at various mobile locations (7895) specified in
the transaction(s). Either the agent(s) completes their function(s)
and terminates (7897), or they discover a need for increased AI to
perform increased functions (7900).
[0388] Once an agent discovers a need for increased AI to perform
increased functions, the agent(s) seek "reserve" layers of AI
(7880). These reserve layer requests are analyzed for minimum
actions necessary (7865) to perform a specific function efficiently
(thereby accommodating mobility requirements), parallel use (7875)
or alternating use (7890) of various AI simultaneously for faster
and effective computation resource capacity utilization. These
added resource capacities are input at the agent interaction level
(7895).
[0389] Once AI requirements are discovered by agents, AI filters
(7910) are employed in which the agent(s) select optimal methods of
AI to employ (7905). For example, an INA may use GP and NN
computing preferences to complete negotiations, while an AA or ITA
may prefer to employ GA to enhance an analytical function. In any
event, the optimal AI method request moves to an agent requesting
reserve AI layers (7880).
[0390] Ultimately, the agent(s) seek out the most effective use of
AI functions (7915). In order to maximize mobility, an agent needs
to determine a (constantly shifting) balance; either (7920) between
less AI program code in order to maintain high speed and light
travel, or increased AI sophistication for intensive analysis or
negotiation activities. In the case of increased mobility with
lighter load (7935), the agent disables unnecessary code
(7930).
[0391] In the case of a need for more program code, increased
calculations are needed for an increased number or complexity of
agent activities (7940). Increasingly complex interactions, e.g.,
with a long negotiation process, may require a different
computation resource type or quantity than an analytical function
that may be more intensely time constrained. In any event, when it
is determined that substantially greater computer resources are
required, the agent(s) may return to the home base in order to
facilitate the request (7945 and 7860).
Analytical Agents
[0392] Analytical agent mobility--from both buyer and seller
viewpoints--is described in FIG. 81. From a seller perspective, the
seller (8000) accesses an S-AA (8030), which receives market data
inputs (8020). The S-AA interacts with S-INAs (8075), S-ITAs (8055)
and, via an S-IA (8045), showcases (8050).
[0393] On the buyer side, the buyer (8005) interacts with a B-AA
(8035) and a CSA (8060) directly, and via a B-IA (8015). Informed
by market data (8025), the B-AA interacts with the CSA (8060),
B-INAs (8070) and B-ITAs (8065).
[0394] Because the system involves autonomous agency with AI, AAs
may be mobile. Both S-AAs and B-AAs use AI, which may involve a
need to return to home base (customer or seller) for increased
computation resources.
[0395] In reference to FIG. 82, kinds of data analysis and
syntheses are described. Whether buyer or seller AAs, various
market data sources (8105) are input. Buyer AAs (8110) perform a
full range of analytical tasks including collaborative filtering
(8120), editorial retailing (8125), expert systems (8130), and
multi-attribute regression analysis (8135). Seller AAs (8115) use
expert systems (8130) and multi-attribute regression analysis
(8135).
[0396] Collaborative filtering uses statistical scenarios (8145),
forecasting (8185) and syntheses (8150) methods, which result in
issuing a recommendation report (8200). Editorial retailing
involves ascertaining third party opinions (8155) and then initial
filtering and synthesis of buyer data (8170), the combination of
the data (8190) and production of a final report (8205). The
reports--typically customized--are made available to B-MNAs and
B-C-MNAs.
[0397] The expert system involves applying pre-programmed
parameters to the mass personalization of data (8160), the use of
targeted information (8175) and the creation of a custom report
(8195).
[0398] Multi-attribute regression analysis typically isolates
variables according to an established relevance scale by using a
process of "factor testing" that measures the accuracy of specific
attributes. At a stable equilibrium point in the analysis, a
synthesis of attributes (8165) can be made that combines key
variables for maximum utility, after which a systematic report is
generated (8180).
[0399] FIG. 83 refers to the analytical agent data flow process.
Market data (8255), from various sources in a distributed computer
system, is translated into codes. AAs (8260) access the market data
and provide a range of services (8265) specified at 8270 and 8275
as well as FIGS. 92.
[0400] In FIG. 84, data mining approaches are described with
particular reference to CSA and AA interactions. Market data (8305)
is fed into the system from different sources, while various
methods of data discovery are employed (8310). For the CSA, these
methods include: search (8315)--including both local (8340) and
global (8375) databases--specific (8320) query (by item (8345) and
by company (8350) accessible from showcases (8380)), general (8325)
query (by category (8355) and industry (8360)), time sensitive
query (8330) and targeted information collection (8335) (that may
involve specific customer requirements (8370)). In addition to
these methods, AAs use data syntheses (8385) that create specific
customer profiles (8400), and data analysis (8390) that features
predictions (8395) and creates scenarios (8410). Whether using
analysis or syntheses, a report is created for AA use.
[0401] A process for advanced collaborative filtering for
cross-marketing recommendations is described in FIG. 85. The
customer requests information on item(s) of interest (8470). The
collaborative filtering process (8490) sorts categories according
to item type, popularity, region, quality, services, bundles,
quantity, price range, and combinations of these categories.
[0402] The customer request for information is analyzed (8500) and
new items of customer interest are statistically ranked in relation
to the initial item request. Other items are recommended that are
associated with information on the current item (8510). A list of
recommended items is presented to the customer (8515) who
subsequently acquires them (8525).
[0403] After the transaction, customer-purchasing habits are
analyzed (8475), in conjunction with interaction with an S-AA
(8460), and the information is fed to the filtration analysis
(8490). Promotions are "pushed" to customers as recommended items
(8505) related to future requested items. This data is input to a
B-AA (8495) and, via a B-IA (8485), to a CSA (8480). In this way,
promotions can be optimally guided after a CSA requests showcase
data (8455) when informed by B-AA (8465).
[0404] This collaboration filtering process is both automated and
mobile because AAs perform these functions interactively at various
locations.
[0405] FIG. 86 shows B-AA operations with mobility. After a B-INA
(or B-ITA) requests analysis from a B-AA (8550), the B-AA activates
analysis functions at a specific location (8555). The B-AA then
moves to a remote or multiple remote locations to collect data (or
it may import data at its home location) (8560).
[0406] The B-AA performs analysis on data (8570), organizes the
analysis and issues a report (8575). In addition, the B-AA also
performs synthesis on the data by combining data sets (8580) and
organizing the data synthesis into a report (8585). The data
analysis and synthesis functions are then applied by the B-AA to a
B-INA (or BITA) function at a specific remote location by exporting
reports (8590) and then closes the session (8600).
[0407] In an additional embodiment, AA functions may be
consolidated with or included in INAs or ITAs and may execute
concurrently.
[0408] FIGS. 87 through 91 describe a system involving services and
service variables that utilize a distinctive code process. Most
agent interaction involves the exchange of information using these
codes.
[0409] FIG. 92 lists services provided in the present system.
[0410] The system represented by the present invention has numerous
distinctive embodiments. The present disclosures illustrate in
detail the main ideas of the invention and are not intended to
restrict the invention to a single embodiment.
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