U.S. patent application number 11/888851 was filed with the patent office on 2007-12-13 for initial product offering system and method.
This patent application is currently assigned to Techventure Associates, Inc., a Delaware corporation. Invention is credited to Rahul R. Vaid.
Application Number | 20070288330 11/888851 |
Document ID | / |
Family ID | 38823040 |
Filed Date | 2007-12-13 |
United States Patent
Application |
20070288330 |
Kind Code |
A1 |
Vaid; Rahul R. |
December 13, 2007 |
Initial product offering system and method
Abstract
A system and method for making an initial product offering of
tangible products and services. A price for a product may be
determined by calculating an aggregate customer history factor by
aggregating customer history factors of potential purchasers in a
buying group, calculating a cumulative demand for a product as a
function of (i) expected purchase quantities of the product
indicated by the potential purchasers in the buying group and (ii)
the aggregate customer history factor, and setting a price of the
product as a function of the cumulative demand for the buying
group. The tangible products and services may be offered at the
determined price.
Inventors: |
Vaid; Rahul R.; (Dallas,
TX) |
Correspondence
Address: |
FISH & RICHARDSON P.C.
P.O. BOX 1022
MINNEAPOLIS
MN
55440-1022
US
|
Assignee: |
Techventure Associates, Inc., a
Delaware corporation
|
Family ID: |
38823040 |
Appl. No.: |
11/888851 |
Filed: |
August 2, 2007 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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11515113 |
Sep 1, 2006 |
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11888851 |
Aug 2, 2007 |
|
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09649224 |
Aug 25, 2000 |
7103565 |
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11515113 |
Sep 1, 2006 |
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60150993 |
Aug 27, 1999 |
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Current U.S.
Class: |
705/80 ;
705/26.2; 705/38 |
Current CPC
Class: |
G06Q 50/188 20130101;
G06Q 30/0254 20130101; G06Q 30/0605 20130101; G06Q 30/06 20130101;
G06Q 30/02 20130101; G06Q 30/0278 20130101; G06Q 40/025
20130101 |
Class at
Publication: |
705/026 ;
705/001; 705/038 |
International
Class: |
G06Q 30/00 20060101
G06Q030/00; G06F 17/00 20060101 G06F017/00 |
Claims
1-21. (canceled)
22. A system for providing group buying comprising: means for
storing a plurality of product parameters, each product parameter
comprising product features; means for searching through a
plurality of products; means for aggregating, at a network server,
a plurality of indications of interest to purchase a product; means
for suspending aggregation of the plurality of indications of
interest; means for preparing a demand packet, each demand packet
comprising aggregated purchasing information from the plurality of
indications of interest; means for routing the demand packet, each
recipient pre-determined in a product network database as
fulfillment destinations; means of receiving offers from
pre-determined fulfillment destinations, the offer consisting of a
price; means for negotiating a price for the item with the
plurality of fulfillment destinations for the item, based on the
demand-packet database; means for selecting one or more of
fulfillment destinations based on the offer, the offer including
the price; means for generating a confirmed purchaser list, the
confirmed purchaser list including an amount of the item equal to a
number of items for which an indication of interest was confirmed;
and means for executing a purchase transaction from the supplier to
the user at the price of the offer.
23. The system of claim 22 wherein the group buying means comprises
a means for interactively performing group buying over a network,
such as the Internet.
24. The system of claim 22 wherein the group buying means further
comprises a means for interactively performing group buying through
a plurality of network devices, such as personal computer, mobile
computer, wearable computer, laptop computer, mobile phone,
wireless computer, personal digital assistant, and handheld
device.
25. The system of claim 22 further comprising means of routing the
demand packet to pre-determined fulfillment destinations.
26. The system of claim 22 further comprising a means for
periodically updating the routing details for the pre-determined
fulfillment destinations.
27. The system of claim 22 further comprising a means for securely
communicating with a credit processor to provide credit solutions
for the purchase transaction.
28. The system of claim 22 further comprising a means for securely
communicating with a credit authorization system to consummate the
purchase transaction.
29. A group buying method comprising: aggregating, at a network
server, a plurality of indications of interest to purchase an item,
storing and creating a demand packet from the plurality of
indications of interest; suspending aggregation of the plurality of
indications of interest; preparing a demand-packet database
including each one of the plurality of indications of interest
received prior to suspending aggregation; routing the demand packet
from the demand-packet database to a plurality of pre-determined
fulfillment destinations; negotiating a price for the item with the
plurality of fulfillment destinations for the item; receiving an
offer from the plurality of fulfillment destinations for the item,
the offer including a price; selecting one or more fulfillment
destinations based on the offer, the offer including the price;
confirming each one of the indications of interest with the user
that supplied the indications of interest at the price of the offer
from the fulfillment destinations; selecting one or more
fulfillment destinations to fulfill the demand packet transaction
based on the re-confirmation of the indications of interest;
generating a confirmed purchaser list, the confirmed purchaser list
including an amount of the item equal to a number of items for
which an indication of interest was re-confirmed; and executing a
purchase transaction from one or more of the selected fulfillment
destinations to the user at the price of the offer.
30. The method of claim 29 wherein the step of conducting group
buying further comprises the option for credit processing through a
credit processor.
31. The method of claim 29 wherein the step of executing a purchase
transaction comprises credit authorization, shipping processing,
and transaction confirmation.
32. The method of claim 29 further comprising the option to create
a group buying offering for a product by storing product parameters
in the product database.
33. The method of claim 29 further comprising the option to search
through stored parameters of products and associated offerings in
the product database.
Description
CROSS REFERENCE TO RELATED APPLICATIONS
[0001] This application claims the benefit of co-pending U.S.
patent application Ser. No. 09/649,224 filed on Aug. 25, 2000,
which claims priority to U.S. Provisional Patent Application No.
60/150,993, filed on Aug. 27, 1999, the entire contents of both
applications are incorporated herein by reference.
BACKGROUND OF THE INVENTION
[0002] 1. Field of the Invention
[0003] The invention relates generally to digital commerce and
specifically to the pricing and sale of products and services
through a network based product offering using demand packets.
[0004] 2. Description of Related Art
[0005] Individual buyers of both consumer and business related
products and services (identified hereinafter as "singular
customer") are at a disadvantage when making purchases because
there is little negotiating leverage for a single sale. Large
volume purchasers, on the other hand, have substantial leverage,
such as when a retailer purchases goods from a wholesale supplier.
There is a continuing need for a purchasing system that provides
the leverage of large volume purchasing interest to singular
customers, while disintermediating the sales chain of the
product.
[0006] One approach to providing a solution for volume and
pre-determined pricing curve based system for the aggregation of
purchasing interest is outlined in U.S. Pat. No. 6,047,266 entitled
"Demand Aggregation through Online Buying Groups." This patent
describes a method wherein an online buying group, referred to as a
"co-op" is formed for the specific purpose of purchasing a
particular product based on a predetermined pricing curve that is
modified by the market data from the co-op. However, as significant
disadvantages, (i) the seller has to disclose to the demand
aggregation system its pricing curve which may be trade secret
information instead of dynamically providing the pricing for the
product, (ii) the system targets the co-op information to "a"
particular vendor or manufacturer of the product, (iii) the system
does not provide existing market-wide price transparency, and (iv)
the system does not allow potential buyers to create their own said
co-ops as the co-ops tend to be driven by the system and
effectively by the pricing curve information provided by the vendor
or the manufacturer.
[0007] Another approach to effectuating bilateral buyer-driven
commerce through allowing prospective buyers to communicate a-
binding purchase offer globally to potential sellers, for sellers
to conveniently search for potential buyer purchase offers, and for
sellers to bind a buyer to its offer is outlined in U.S. Pat. No.
5,794,207, entitled "Method and Apparatus for a Cryptographically
Assisted Commercial Network System Designed to Facilitate
Buyer-Driven Conditional Purchase Offers." This patent describes a
method and system whereby buyers can negotiate a purchase price of
a product or service with a seller through an online bid-offer
system. However, as a significant disadvantage, the patent does not
create buying groups that have the ability of large volume
discounts.
[0008] There remains the need for a digital commerce system that
allows singular customers to create their own demand or purchasing
interest pools, and routes these packets of demand ("demand
packets") to a plurality of hosts comprising (i) multiple
suppliers, vendors, manufacturers and distributors of the
particular product or service, (ii) auction networks where these
demand packets may represent both sell and buy-side entries, (iii)
vertical exchanges where similar category of products and services
are sold and brokered, and (iv) horizontal marketplaces where
similar categories of products and services are sold and brokered.
There also remains the need for this system to be available over a
plurality of network access devices comprising mobile phones,
mobile computers, personal computers, laptop computers, handheld
computers, personal digital assistants, and handheld computers. The
system should further provide optimal pricing for the products
coupled with market-wide price transparency.
SUMMARY OF THE INVENTION
[0009] To overcome the problems of conventional demand and supply
aggregation systems, the principles of the present invention
provide for a method and for determining a price for a product. The
method may include calculating an aggregate customer history factor
by aggregating customer history factors of potential purchasers in
a buying group. The customer history factors may include at least
one of the following customer related parameters: customer rating
parameter, customer transaction parameter, customer demographics
parameter, customer geographics parameter, customer psychographic
parameter, and customer behavioral parameter. The method further
calculates a cumulative demand for a product as a function of (i)
expected purchase quantities of the product indicated by the
potential purchasers in the buying group and (ii) the aggregate
customer history factor. A price of the product may be set as a
function of the cumulative demand for the buying group.
[0010] The system and method of the invention will be more readily
understood and apparent from the following detailed description of
the invention when read in conjunction with the accompanying
drawings, and from the claims, which are appended at the end of the
detailed description.
BRIEF DESCRIPTION OF THE DRAWINGS
[0011] The foregoing will be appreciated more fully from the
following further description thereof, with reference to the
accompanying drawings, wherein:
[0012] FIG. 1 is an abstract diagram of a system for group buying
according to the invention;
[0013] FIG. 2 illustrates records in a product database;
[0014] FIG. 3 illustrates records in a conditions of indications of
interest database;
[0015] FIG. 4 illustrates records in a demand packet database;
[0016] FIG. 5 illustrates records in a product pricing
database;
[0017] FIG. 6 illustrates records in a customer sales database;
[0018] FIG. 7 illustrates records in a product network.
database;
[0019] FIG. 8 shows the description of the demand packet;
[0020] FIG. 9 is a block diagram of a network system server;
[0021] FIG. 10 is a block diagram of a demand packet server;
[0022] FIG. 11 is a block diagram of a transaction server;
[0023] FIGS. 12A-12B are a flow chart of a customer process in the
product offering for group buying according to the invention;
[0024] FIGS. 13A-13E are a flow chart of a system process in the
product offering for group buying according to the invention;
[0025] FIG. 14 is a block diagram of an exemplary system 1400 for
determining a customer history factor from various input variables
or parameters; and
[0026] FIG. 15 is a block diagram of an exemplary neural network
diagram that may be used to predict whether a potential buyer will
become an actual buyer after submitting an indication of interest
based on customer and economic related variables.
DETAILED DESCRIPTION
[0027] To provide an overall understanding of the invention,
certain illustrative embodiments will now be described, including
demand packets and a system and method for group buying using same
demand packets. However, it will be understood by those of ordinary
skill in the art that the demand packet may be adapted to other
forms, physical and virtual, provided they are capable of including
the necessary demand packet parameters described below. It will
also be understood that the methods and systems described herein
can be suitably adapted to any other sales model where a customer
can make an indication of interest while reconfirming based on
trigger events, such as, for example, reconfirm automatically if
the price of the product is in the given range. The terms "product"
and "item" are used interchangeably herein to denote products of
all kinds comprising products, services, consumer products, and
solutions in all physical and abstract forms. The terms "customer",
"purchaser", and "operator" are used interchangeably herein to
denote a potential buyer, which places an indication of interest to
purchase the given product.
[0028] Overview of System
[0029] FIG. 1 shows a preferred embodiment of a system 100 in
accordance with the present invention, where a mobile computer user
105, wireless device user 110, personal computer user 115, personal
computer user 120, laptop computer user 125, handheld device user
130, and personal digital assistant user 135 (collectively, the
"User") through a network 140 such as the Internet connects to a
network system server 145, which is in direct communications with a
demand packet server 150. The demand packet server 150 is further
in direct communications with a transaction server 155 comprising
four servers: a supplier server 160, an auction server 165, a
vertical exchange server 170, and a horizontal marketplace server
175. The supplier server 160 is further in direct communications
with a plurality of suppliers for the product comprising
manufacturers 180 and distributors 182. The auction server 165 is
further in direct communications with a plurality of auction
venues, where the demand packet may be auctioned, comprising
auction site #1 184 and auction site #N 186. The vertical exchange
server 170 is further in direct communications with a plurality of
vertical exchanges for the product comprising vertical exchange #1
188 and vertical exchange #N 190. The horizontal marketplace server
170 is further in direct communications with a plurality of
horizontal marketplaces for the product comprising horizontal
marketplace #1 192 and horizontal marketplace #N 194. Generally,
the configuration of the system 100 allows the network system
server 145 to present the User with product information on a
specific product offering in a computer searchable form and the
ability for the User to create a new product offering through the
system.
[0030] As will be discussed in more detail below, the User can
select a product offering through the network system server 145 and
place an indication of interest. The network system server 145
collects additional indications of interest in the same product
offering while pre-determined conditions are fulfilled (e.g., time
frame for the product offering). After suspension of the
collections of the indications of interest for the product
offering, the network system server 145 transmits the indications
of interest to the demand packet server 150. The demand packet
server 150 processes the received information and communicates with
the transaction server 155. Based on the indications of interest
transmitted by the demand packet server 150 in the form of demand
packets and certain pre-determined conditions of the product
offering, the transaction server 155 presents to the User via the
demand packet server 150 and the network system server 145, offers
from one or more product fulfillment destinations for the specific
product offering.
[0031] In the communicating relationship, the network system server
145 collects reconfirmation at the offer price for all the Users
that had previously placed an indication of interest in the
specific product offering. Upon receiving the reconfirmations, the
system 100 negotiates the offer price with the plurality of
fulfillment destinations that had presented the offer, selects the
final offer price, the transaction server 155 reaffirms the offer
price and notifies the User via the demand packet server 150 and
the network system server 145, regarding parameters for
consummating the purchase transaction (e.g., physical shipping of
the product, electronic delivery of the service).
[0032] Software Databases
[0033] A product database 200 stores product parameters for a
plurality of product offerings. As shown in FIG. 2, the product
database 200 stores a plurality of records 250, each record
including a plurality of fields, the plurality of fields
comprising: a product identifier 205 uniquely identifying the
product in the product offering, the vendor name 210 identifying
the manufacturer of the product, the product name 215 identifying
the name of the product, the product description 220 identifying
the description of the product, the release date 225 identifying
the date on which the product will be released into the market, the
available date 230 identifying the date on which the product will
be available through the system 100, the MSRP 235 identifying the
manufacturer's retail price for the product, the pricing range max
240 identifying the maximum price of the product for the product
offering, and the pricing range min 245 identifying the minimum
price of the product for the product offering. For example,
referring to record 255, the product 101, which is an automobile is
manufactured by Ford and termed Toy 2000. Furthermore, this record
255 indicates that the release date of the product is December 1999
and its available date on the system 100 is December 1999 as well.
The manufacturers retail price for the product is $20,000, its
maximum range is $20,000 and its minimum pricing range is
$14,000.
[0034] A conditions of indications of interest database 300 stores
the conditions upon which the product offering shall be conducted
for the product. As shown in FIG. 3, the conditions of indications
of interest database 300 stores a plurality of records 330, each
record including a plurality of fields, the plurality of fields
comprising: a product identifier 205 uniquely identifying the
product in the product offering, the conditions of indications of
interest 305 comprising: the pricing date 310 identifying the date
on which the product offering will be priced, the min # indications
315 identifying the minimum number of indications of interest that
will be received to conduct the product offering, and other
conditions 320 and 325 as may be deemed appropriate by the creator
of the product offering. For example. referring to record 335, the
product 101 will be priced on Dec. 1, 1999 if at least 10
indications of interest to purchase the product are received by the
system 100.
[0035] A demand packet database 400 stores the parameters that form
a demand packet for each product offering. As shown in FIG. 4, the
demand-packet database 400 stores a plurality of records 415, each
record including a plurality of fields, the plurality of fields
comprising: a demand-packet identifier 405 uniquely identifying the
demand packet, the demand packet destination identifier 705
uniquely identifying the pre-determined fulfillment destinations
where the demand packet is to be routed, the product identifier 205
identifying the product for which the demand packet has been
created, the customer identifier 605 identifying the customer
indicating the demand for the given demand packet, the indication
date 615 identifying the date on which the demand was indicated by
the customer into the demand packet database, the quantity 620
identifying the number of items of the product which are sought,
the cumulative demand size 410 identifying the cumulative size of
the demand, the customer history factor 635 identifying the average
purchase rate of the customer, and the pricing date 310 identifying
the date on which the product offering will be priced. For example,
referring to record 420, the demand packet A2004 has a demand
destination of DD20 and the packet is being created for a product
123 where an indication of interest is placed on Apr. 15, 2000 by
customer C002 with a customer history factor of 68% for a quantity
of 2 items. At this record 420, the cumulative demand size for the
product offering is 2 and the offering will be priced on Jun. 22,
2000.
[0036] A product pricing database 500 stores, for each product
offering, the pricing date along with the final offer from the
fulfillment destination on the pricing date. As shown in FIG. 5,
the product pricing database 500 stores a plurality of records 510,
each record including a plurality of fields, the plurality of
fields comprising: a product identifier 205 uniquely identifying
the product in the product offering, the release date 225
identifying the date on which the product will be released into the
market, the available date 230 identifying the date on which the
product will be available through the system 100, the MSRP 235
identifying the manufacturer's retail price for the product, the
vendor price 505 identifying the final offer price from the
fulfillment destination that is selected in conjunction with
fulfilling the purchase transaction for the product offering, and
the pricing date 310 identifying the date on which the product
offering will be priced. For example, referring to record 515, the
product 101 will be priced on Dec. 28, 1999 at a vendor price of
$16,000.
[0037] A customer sales database 600 stores, for each customer, the
details of interaction with the system 100 for specific product
offerings. As shown in FIG. 6, the customer sales database 600
stores a plurality of records 655, each record including a
plurality of fields, the plurality of fields comprising: a customer
identifier uniquely identifying the customer, the customer name,
the product identifier 205 uniquely identifying the product in the
product offering, the date of indication 615 identifying the date
on which the customer placed the indication of interest for the
product offering, the quantity 620 identifying the number of items
the indication of interest is being placed for, the date of
reconfirmation 625 identifying the date on which the customer
reconfirmed its indication of interest, the shipping info 630
identifying the address the product is being shipped, the billing
info 635 identifying the method of payment for the product, the
customer history factor identifying the average purchase rate of
the customer for past product offerings, the date shipped 645
identifying the date on which the product was shipped to the
customer, and the comments 650 on interactions with the customer.
For example, referring to record 660, the customer c001 identified
as Joe Schmoe placed an indication of interest of 2 items for
product 101 on Nov. 28, 1999 and reconfirmed the indication of
interest on Dec. 28, 1999. Referring further to record 660, the
customer c001 asked for the product to be sent to 11 Walker Drive,
New York, N.Y. 10000, provided a credit card # as his preferred
method of payment where the product was shipped on Jan. 7, 2000.
Referring further to record 660, the customer c001 has a customer
history factor of 54%, and there have been interactions with the
customer.
[0038] A product network database 700 stores, for each product, the
network of preferred and pre-determined fulfillment destinations
for specific product offerings. As shown in FIG. 7, the product
network database 700 stores a plurality of records 730, each record
including a plurality of fields, the plurality of fields
comprising: a product identifier 205 uniquely identifying the
product in the product offering, the vendor name 210, the product
name 215, the demand destination identifier 705 uniquely
identifying the preferred and pre-determined fulfillment
destinations for the product in the product offering, the supplier
table 710 that stores a plurality of records 755, each record
including a plurality of fields, the plurality of fields
comprising: S102 735 identifying the electronic addresses of the
preferred and pre-determined fulfillment destinations in the
supplier category, the auction table 715 that stores a plurality of
records 755, each record including a plurality of fields, the
plurality of fields comprising: A102 740 identifying the electronic
addresses of the preferred and pre-determined fulfillment
destinations in the auction category, the vertical exchange table
720 that stores a plurality of records 755, each record including a
plurality of fields, the plurality of fields comprising: V102 745
identifying the electronic addresses of the preferred and
pre-determined fulfillment destinations in the vertical exchange
category, and the horizontal marketplace table 725 that stores a
plurality of records 755, each record including a plurality of
fields, the plurality of fields comprising: H102 735 identifying
the electronic addresses of the preferred and pre-determined
fulfillment destinations in the horizontal marketplace category.
For example, referring to record 760, for product 101 with the name
Toy 2000 to be supplied by Ford, the demand destination identifier
is DD01 which is sent to pre-determined fulfillment destinations
S102 for the supplier category, A102 for the auction category, V102
for the vertical exchange category, and H102 for the horizontal
marketplace category.
[0039] The Demand Packet
[0040] FIG. 8 shows a preferred embodiment of the demand packet
800, which is created by the demand packet server 150 from the
network based aggregated information collected by the network
system server 145. The demand packet 800 is utilized by the demand
packet server 150 to communicate with the transaction server 155
and its sub servers in order to consummate the purchase
transaction. Each demand packet 800 stores a plurality of relevant
records comprising: the demand packet identifier 405, the demand
destination identifier 705, the product identifier 102, the pricing
date 310, the demand size 805 uniquely identifying the cumulative
size of the demand collected through all the indications of
interest times the quantity 620 in the demand packet database 400,
the customer history factor 810 identifying the average purchase
rate of all the customers that placed indications of interest for
the product offering, the expected demand 815 identifying the
multiplication of the demand size 805, and the customer history
factor 810, the vendor name 210, the product description 215, the
product MSRP 235, the product pricing max 240, the product pricing
min 245, the product vendor price 505, and the conditions of
indication of interest 305 based upon which the potential customers
had placed their indication of interest further comprising: the
pricing date 310, the min # indications 315, the condition 3 320,
and the condition 4 325.
[0041] Hardware Servers
[0042] FIG. 9 is a block diagram of a network system server 145.
The network system server 145 includes a processor 920, and
connected thereto, a random access memory 910, a read-only memory
905, a network card 915, a system clock 930, and a storage device
935. The network card 915 can be any network card capable of
handling numerous logical connections 925 to a network 945, as
required by the number of customers, fulfillment destinations,
financial transaction processors, and logical connections 950 to
the demand packet server requiring resources from the network
system server 145. The storage device 935 can be any storage device
capable of maintaining a product database 200, a customer sales
database 600, and financial and credit processor 940, such as a
hard drive, storage area network, redundant array of inexpensive
disks, or other mass storage device. If the databases 200, 600 on
the storage device 935 are particularly large, a separate
transaction processor may be provided to off-load database
management from the processor 920. The processor 920 and memories
910, 905 may be any processor and memories known in the art that
are consistent with the volume of traffic handled by the network
card 915, including any associated security protocols, and the
volume of data stored in the storage device 935. Suitable network
servers are manufactured by Compaq Computers, Dell, IBM, and Sun
MicroSystems. Such servers may employ a processor with multiple
central processing units, and will operate under control of an
operating system such as Unix, Linux, other Unix variants, DOS,
Windows or its variants, VMS, and Solaris. The network system
server 145 will also run additional programs or software modules
from the operating system to control server operations, web server
operations, authentication functions, network security, and
database management, many alternatives for which are known in the
art and commercially available. The invention may be usefully
practiced with any of these computers, operating systems, and other
programs. The software modules will also provide and operate a web
site provided by the network system server 145 for the customers,
according to information stored on the storage device 935.
[0043] FIG. 10 is a block diagram of a demand packet server 150.
The demand packet server 150 includes a processor 1020, and
connected thereto, a random access memory 1010, a read-only memory
1005, a network card 1015, a system clock 1025, and a storage
device 1045. The network card 1015 can be any network card capable
of handling numerous logical connections 1035 to a network 1055, as
required by the number of customers, fulfillment destinations,
demand packet processors, logical connections 1030 to the network
system server 145, and logical connections 1040 to the transaction
server 155 requiring resources from the demand packet server 150.
The storage device 1045 can be any storage device capable of
maintaining a product pricing database 500, a conditions of
indications of interest database 300, a demand packet database 400,
and a demand packet processor 1050, such as a hard drive, storage
area network, redundant array of inexpensive disks, or other mass
storage device. If the databases 300, 400, 500 on the storage
device 1045 are particularly large, a separate transaction
processor may be provided to off-load database management from the
processor 1020. The processor 1020 and memories 1010, 1005 may be
any processor and memories known in the art that are consistent
with the volume of traffic handled by the network card 1015,
including any associated security protocols, and the volume of data
stored in the storage device 1045.
[0044] Suitable network servers are manufactured by Compaq
Computers, Dell, IBM, and Sun MicroSystems. Such servers may employ
a processor with multiple central processing units, and will
operate under control of an operating system such as Unix, Linux,
other Unix variants, DOS, Windows or its variants, VMS, and
Solaris. The demand packet server 150 will also run additional
programs or software modules from the operating system to control
server operations, web server operations, authentication functions,
network security, demand packet processing and database management,
many alternatives for which are known in the art and commercially
available. The invention may be usefully practiced with any of
these computers, operating systems, and other programs.
[0045] FIG. 11 is a block diagram of a transaction server 155. The
transaction server 155 includes a processor 1120, and connected
thereto, a random access memory 1110, a read-only memory 1105, a
network card 1115, a system clock 1125, and a storage device 1145.
The network card 1115 can be any network card capable of handling
numerous logical connections 1130 to a network 1175, as required by
the number of customers, fulfillment destinations, and transaction
processors 1155, 1160, 1165, 1170, logical connections 1135 to the
network system server 145, and logical connections 1140 to the
demand packet server 150 requiring resources from the transaction
server 155. The storage device 1145 can be any storage device
capable of maintaining a product network database 700, a supplier
processor/server 1155, an auction processor/server 1160, a vertical
exchange processor/server 1165, and a horizontal marketplace
processor/server 1170, such as a hard drive, storage area network,
redundant array of inexpensive disks, or other mass storage device.
If the database 700 on the storage device 1145 are particularly
large, a separate transaction processor may be provided to off-load
database management from the processor 1120. The processor 1120 and
memories 1110, 1105 may be any processor and memories known in the
art that are consistent with the volume of traffic handled by the
network card 1115, including any associated security protocols, and
the volume of data stored in the storage device 1145. Suitable
network servers are manufactured by Compaq Computers, Dell, IBM,
and Sun MicroSystems. Such servers may employ a processor with
multiple central processing units, and will operate under control
of an operating system such as Unix, Linux, other Unix variants,
DOS, Windows or its variants, VMS, and Solaris. The transaction
server 155 will also run additional programs or software modules
from the operating system to control server operations, web server
operations, authentication functions, network security, fulfillment
processing, and database management, many alternatives for which
are known in the art and commercially available. The invention may
be usefully practiced with any of these computers, operating
systems, and other programs.
[0046] Method of Operation
[0047] An embodiment of the process for the system 100 described
above will now be described in detail by reference to FIGS. 12A-12B
and FIGS. 13A-13E.
[0048] Customer Process
[0049] FIGS. 12A-12B are flow charts showing a customer's
interaction process 1200 with the system 100 for group buying
according to the invention, which also shows the resources used for
each step. The customer process 1200 begins when the customer logs
on to a secure web site 1205 that is provided by the network system
server 145. Once the customer is logged on to the system 1205, the
customer interactively browses lists of available product
categories 1210, each product category identifying an offering for
that product from the associated product database 200, for product
offerings that may be of interest to the customer. The network,
system server 145 maintains communication with the product database
200, which is periodically updated to add and remove product
offerings. All web server communications are secure, such as
through the secure socket layer (SSL) communications through
digital encryption identifications through commercially available
services such as Verisign. After the customer has had a chance to
view the product categories and select a desired product 1210, the
customer is asked if there is an interest in placing a conditional
indication of interest in purchasing the product as part of an
offering group 1215. This entails two options 1215: (1) no interest
in placing an indication of interest wherein the customer has an
option to create their own product offering for a specific product
1220 and then view product categories to select the product 1210,
or view product categories once again to select a desired product
1210, and (2) place indication of interest for the desired product
1225 which is entered into the 10 secure demand packet database
400, and, if the offering is still open, the customer receives
e-mail confirmation of the accepted indication of interest.
Indications of interest to purchase an item are aggregated into a
group, along with available information about customers, such as
their histories of purchasing through the demand packet server.
This group information is then used to negotiate a group purchase
price with potential fulfillment destinations. The negotiation may
occur through human interaction between the web provider and the
fulfillment destinations, or may occur using a pre-determined
protocol between the web server and a remote server operated by the
fulfillment destinations. During the entire negotiation process,
the customer waits for receiving the offer from one fulfillment
destination or multiple offers from a plurality of fulfillment
destinations 1230. Once a price is negotiated, each customer who
indicated an interest to purchase receives an e-mail detailing the
offer 1230. The offer is sent to the customer in accordance with
the offer entry in the product pricing database 500. At this point,
the customer may accept the offer and proceed with a purchase
transaction, or the customer may reject the offer. If the customer
chooses to accept the offer(s), the customer is required to
reconfirm the offer(s) within a specified amount of time as
indicated in the customer's offer e-mail 1235. The reconfirmation
process 1235 is conducted in accordance with information exchange
between the network system server 145, the demand packet server
150, and the demand packet database 400. Where an offer is rejected
by one or more customers, this information may optionally be stored
and used to attempt another round of price negotiation. After the
specified time frame, the reconfirmations are updated in the demand
packet database 400 for the entire group that had previously placed
an indication of interest. The reconfirmations may be of multiple
nature meaning that one customer may reconfirm for multiple offers
that he may have received from a plurality of fulfillment
destinations. The aggregate reconfirmation group information is
re-presented to the fulfillment destinations that had provided the
offer, and the final offer price from one or more fulfillment
destinations is provided to the customers 1240. At this point, if
the customer desires credit processing 1245, the customer is
required to enter credit processing information 1250 that is
exchanged with a credit processor 940 for the purposes of providing
a credit solution to the customer. In the following step, the
customer is required to enter product delivery information such as
the shipping information 1260 which is updated into the customer
sales database 600 which marks the completion of the purchase
transaction 1265 on the system 100.
[0050] FIGS. 13A-13E are flow charts showing a system process 1300
for group buying according to the invention, which also shows the
resources used for each step. The system process 1300 begins when a
user, comprising a customer or one of the plurality of fulfillment
providers, logs on the system. If the user is one of the plurality
of fulfillment providers, the web site offers two options: (1) to
view an existing demand packet, as will be discussed later, and (2)
create a new product offering. To create a new product offering.
the fulfillment destination selects products 1305 and provides
product specific information 1310 including a manufacturer
suggested retail price (MSRP), a range of offering prices,
conditions for the offering, and a time period for the offer. Once
an interested user has provided the required information, a product
listing may be added 1315 to the product database 200, and the
conditions of indication of interest to be added to the conditions
of indication of interest database 300. The system 100 then
interacts with customers and collects the indications of interest
1320 as described above until any conditions of indication of
interest set forth by the fulfillment destination have been
satisfied 1325. In step 1320, the system 100 exchanges the customer
specific information with the demand packet database 400. In step
1325, the system 100 exchanges the indication of interest
information with the conditions of indication of interest database
300. If the fulfillment destination's conditions have not been
satisfied by the pricing date, the fulfillment destination may
change the conditions. At this point, the system 100, in accordance
with the demand packet database 400, creates a demand packet 1330.
Depending on the preferred and pre-determined fulfillment
destinations for the demand packet, the demand packet may be routed
to one or more of fulfillment processes including: a supplier
process for a plurality of supplier destinations 1335, an auction
process for a plurality of auction destinations 1340, a vertical
exchange process for a plurality of vertical exchange destinations
1345, and a horizontal marketplace process for a plurality of
horizontal marketplace destinations 1350.
[0051] In the supplier process 1335, the supplier server 1155
receives the demand packet 1335.05 and routes the demand packet to
a plurality of supplier category fulfillment destinations 1335.10
prior to which the supplier server 1155 secures routing information
from the product network database 700 and vice versa. Based on the
information contained in the demand packet and the aggregated
indications of interest obtained through the demand packet database
400, one or more supplier category fulfillment destinations
announce the offers which includes the pricing 1335.15. The offers
are entered into a product pricing database 500, and the pricing
information is e-mailed to participating customers for
reconfirmation 1335.20. In a finite time frame, the system 100
collects reconfirmations from the customers that had placed an
indication of interest 1335.25, and this information is provided
for in the demand packet database 400 and the customer sales
database 600. It should be noted that one customer may, for one
previously entered indication of interest, submit multiple
reconfirmations to one or more of the offers received from the
system 100. Upon transmitting the reconfirmations to all the
supplier category fulfillment destinations that had made an offer
based on the demand packet, the pricing is confirmed by the system
100, and, following that, the system 100 negotiates the best price
with one or more supplier category fulfillment destinations. This
set of final offers is compared against offers received from the
auction process 1340, a vertical exchange process 1345, and a
horizontal marketplace process 1350 to determine the final best
price offer 1335.30, which if accepted by the system 100, will
prompt the customers for entering credit information 1335.35 and,
if required 1335.40, connect the customers to a credit processor
940 for credit solutions. As shown in step 1335.45, the system
collects final shipping information and payment from the customer
and stores it into the customer sales database 600. After shipping
and payment information acquisition, the system 100 completes the
purchase transaction and confirms via e-mail the closing of the
purchase transaction with the customers 1335.50.
[0052] In the auction process 1340, the auction server 1160
receives the demand packet 1340.05 and routes the demand packet to
a plurality of auction category fulfillment destinations 1340.10
prior to which the auction server 1160 secures routing information
from the product network database 700 and vice versa. Based on the
information contained in the demand packet and the aggregated
indications of interest obtained through the demand packet database
400, one or more auction category fulfillment destinations announce
the offers which includes the pricing 1340.15. The offers are
entered into a product pricing database 500, and the pricing
information is e-mailed to participating customers for
reconfirmation 1340.20. In a finite time frame, the system 100
collects reconfirmations from the customers that had placed an
indication of interest 1340.25, and this information is provided
for in the demand packet database 400 and the customer sales
database 600. It be noted that one customer may for one previously
entered indication of interest submit multiple reconfirmations to
one or more of the offers received from the system 100. Upon
transmitting the reconfirmations to all the auction category
fulfillment destinations that had made an offer based on the demand
packet, the pricing is confirmed by the system 100, and following
that the system 100 negotiates the best price with one or more
auction category fulfillment destinations. This set of final offers
are compared against offers received from the supplier process
1335, a vertical exchange process 1345, and a horizontal
marketplace process 1350 to determine the final best price offer
1340.30, which, if accepted by the system 100, will prompt the
customers for entering credit information 1340.35 and, if required,
1340.40 connect the customers to a credit processor 940 for credit
solutions. As shown in step 1340.45, the system collects final
shipping information and payment from the customer and stores it
into the customer sales database 600. After shipping and payment
information acquisition, the system 100 completes the purchase
transaction and confirms via e-mail the closing of the purchase
transaction with the customers 1340.50.
[0053] In the vertical exchange process 1345, the vertical exchange
server 1165 receives the demand packet 1345.05 and routes the
demand packet to a plurality of vertical exchange category
fulfillment destinations 1345.10 prior to which the vertical
exchange server 1165 secures routing information from the product
network database 700 and vice versa. Based on the information
contained in the demand packet and the aggregated indications of
interest obtained through the demand packet database 400, one or
more vertical exchange category fulfillment destinations announce
the offers which includes the pricing 1345.15. The offers are
entered into a product pricing database 500, and the pricing
information is e-mailed to participating customers for
reconfirmation 1345.20. In a finite time frame, the system 100
collects reconfirmations from the customers that had placed an
indication of interest 1345.25, and this information is provided
for in the demand packet database 400 and the customer sales
database 600. It should be noted that one customer may, for one
previously entered indication of interest, submit multiple
reconfirmations to one or more of the offers received from the
system 100. Upon transmitting the reconfirmations to all the
vertical exchange category fulfillment destinations that had made
an offer based on the demand packet, the pricing is confirmed by
the system 100, and following that, the system 100 negotiates the
best price with one or more vertical exchange category fulfillment
destinations. This set of final offers is compared against offers
received from the supplier process 1335, the auction process 1340,
and the horizontal marketplace process 1350 to determine the final
best price offer 1345.30 which, if accepted by the system 100, will
prompt the customers for entering credit information 1345.35 and,
if required, 1345.40 connect the customers to a credit processor
940 for credit solutions. As shown in step 1345.45, the system
collects final shipping information and payment from the customer
and stores it into the customer sales database 600. After shipping
and payment information acquisition, the system 100 completes the
purchase transaction and confirms via e-mail the closing of the
purchase transaction with the customers 1345.50.
[0054] In the horizontal marketplace process 1350, the horizontal
marketplace server 1170 receives the demand packet 1350.05 and
routes the demand packet to a plurality of horizontal marketplace
category fulfillment destinations 1350.10 prior to which the
horizontal marketplace server 1170 secures routing information from
the product network database 700 and vice versa. Based on the
information contained in the demand packet and the aggregated
indications of interest obtained through the demand packet database
400, one or more horizontal marketplace category fulfillment
destinations announce the offers which includes the pricing
1350.15. The offers are entered into a product pricing database
500, and the pricing information is e-mailed to participating
customers for reconfirmation 1350.20. In a finite time frame, the
system 100 collects reconfirmations from the customers that had
placed an indication of interest 1350.25, and this information is
provided for in the demand packet database 400 and the customer
sales database 600. It should be noted that one customer may, for
one previously entered indication of interest, submit multiple
reconfirmations to one or more of the offers received from the
system 100. Upon transmitting the reconfirmations to all the
horizontal marketplace category fulfillment destinations that had
made an offer based on the demand packet, the pricing is confirmed
by the system 100, and, following that, the system 100 negotiates
the best price with one or more horizontal marketplace category
fulfillment destinations. This set of final offers are compared
against offers received from the supplier process 1335, the auction
process 1340, and the vertical exchange process 1350 to determine
the final best price offer 1350.30 which, if accepted by the system
100, will prompt the customers for entering credit information
1350.35 and, if required. 1350.40 connect the customers to a credit
processor 940 for credit solutions. As shown in step 1350.45, the
system collects final shipping information and payment from the
customer and stores it into the customer sales database 600. After
shipping and payment information acquisition, the system 100
completes the purchase transaction and confirms via e-mail the
closing of the purchase transaction with the customers 1350.50.
[0055] The customer history factor can be determined in a number of
ways. As previously described, a customer's actual buying history
may be used for determining the customer history factor. As an
example, if a customer submits an indication of interest indicating
that he or she will purchase a product, but actually purchases the
product 20% of the time, that person's actual buying history (i.e.,
customer history factor in one embodiment) is 20%. However, such a
simplistic model for determining a customer history factor can be
expanded to include an unlimited number of variables or parameters
and calculated using a variety of mathematical functions and
statistical models.
[0056] The customer related variables (CRV) may be used in
determining a customer history factor:
[0057] Rating variables: rating variables may include customer
feedback score, customer rating, customer ranking, customer score,
customer feedback rating, and so on. Feedback, such as feedback
ratings, may be used to determine each member's feedback score. A
positive rating may add a value, such as +1, to the customer's
score, a negative rating may decrease a value, such as -1, from the
customer's score, and a neutral rating may have no impact. The
higher the feedback score, the more positive ratings the customer
has received from members. In one embodiment, a member can increase
or decrease another member's score by only .+-.1 no matter how many
transactions they share.
[0058] Customer transaction variables: customer transaction
variables may include most recent purchases, highest monetary
transactions, average monetary transactions, buy rate, sell rate,
return rate, indication rate, indication-to-buy rate, transaction
frequency, transaction initiation rate, transaction close rate, and
so on.
[0059] Demographic variables: demographic variables may include
age, gender, race, education, occupation, income, religion, marital
status, family size, number of children, home ownership status,
socio-economic status, and so on.
[0060] Geographic variables: geographic variables may include
various classifications of geographic areas. For example, the
classifications of geographic areas may include zip code, state,
country, region, climate, population, and other geographical census
data. In one embodiment, this information can come from national
census data. Alternatively, maps, mapping databases, and other
databases as understood in the art may be utilized to determine the
geographic variables. A value may be assigned to the geographic
variables depending on past, current, or future events affecting
the location that a customer lives. For example, if a location is
affected by a natural disaster, such as a hurricane or flood, the
likelihood that the customer will purchase a product after
submitting an indication of interest may be lower or higher
depending on the particular product.
[0061] Psychographic variables: psychographic variables may include
life style, personality, values, attitudes, and so on. These
variables may be useful in predicting whether a customer will
purchase a product after submitting an indication of interest at a
later date. For example, if a customer's life style includes
business travel, then the customer history factor may be decreased
because there is a potential that the customer will be traveling
when the product begins to sell and the customer will be unable to
purchase the product or forget about the product.
[0062] Behavioral variables: behavioral variables may include
product usage rate, brand loyalty, benefit sought, decision making
units, ready-to-buy stage, consistent high-end product purchaser,
and so on. A value may be assigned to the behavioral variables
depending on a number of factors. For example, if a customer
routinely purchases high-end products, then the customer may have a
higher score for the high-end product purchaser variable than
someone who does not routinely purchase high-end products.
[0063] Each of the customer related variables used for determining
a customer history factor may be assigned a value. The values of
the variables may be assigned numeric or alphanumeric values. The
variables may be assigned values manually, semi-automatically, or
automatically based on a variety of factors. The customer related
variables may be used in mathematical functions in computing a
customer history factor.
[0064] In addition to customer related variables, economic related
variables (ERV) may be utilized in accordance with the principles
of the present invention. Economic related variables may be related
to macroeconomic or microeconomic factors. For example,
macroeconomic variables may include household debt service burden,
unemployment, consumer confidence index, producer price index,
productivity report, retail sales index, durable goods orders,
employment cost index, personal bankruptcy filings, inflation rate,
GDP growth rate, S&P 500 stock market index, and so on.
Microeconomic variables may be related to supply and demand related
as to individual consumers and businesses in a local region, for
example. Microeconomic variables may include supply of current
certain products, current demand of certain products, local economy
growth rate, consumer job status, consumer disposable income, and
so forth. Applying microeconomic theory, if a particular region of
the country is predicted to have a hurricane, certain products may
be more important than others, such as flashlights, wood, and
water. If a consumer has indicated that he or she wants to purchase
a bedroom set, a value of a variable related to the purchase on a
microeconomic level may be lowered.
[0065] FIG. 14 is a block diagram of an exemplary system 1400 for
determining a customer history factor from various input variables
or parameters. The input parameters may be stored in one or more
databases stored on a storage system (e.g., hard drive of a
server). In one embodiment, the parameters may include rating
parameters 1402, transaction parameters 1404, demographic
parameters 1406, geographic parameters 1408, psychographic
parameters 1410, and behavioral parameters 1412. Other and/or
different parameters may be utilized in accordance with the
principles of the present invention. One or more estimation
engine(s) 1414 may be utilized to determine a customer history
factor 1416 based on one or more parameters 1402-1412. The
estimation engine(s) 1414 may be simple algebraic formulas (e.g.,
multiplication and addition) or more extensive logic and/or
formulaic approaches, as described hereinbelow.
[0066] A customer history factor may be computed in an unlimited
number of ways using the consumer related variables and economic
related variables. Some mathematical computations and modeling
approaches are provided below. It should be understood that these
computations and approaches are exemplary and that other
computations and approaches may be used to compute the customer
history factor in accordance with the principles of the present
invention. Furthermore, the customer related variables and economic
related variables may be used in the computations and models in any
combination that is helpful in determining the customer history
factor to provide a more accurate estimate of how many units of a
product to produce based on the indications of interest received
from a new product offering. Below are exemplary mathematical
models and formulas that may be used to compute the customer
history factor using the consumer and/or economic related
variables:
[0067] Linear Approach:
[0068] The linear models may be structured in the form of a
cumulative model using customer and/or economic related variables
or selective data models. Industry selective data models, such as
recency, frequency, monetary (RFM) models and chi-squared automatic
interaction detection (CHAID) analysis may be used wherever
appropriate. The CHAID analysis can incorporate recency, frequency
and monetary variables, but can also examine other variables to
increase predictive power. One embodiment may compute a customer
history (CHF) as: CHF %=CRV % * ERV % where CRV %=.SIGMA.{CRV(1) .
. . CRV(n)}/n or CRV %=.SIGMA.{CRV(1)/x . . . CRV(n)/z} ERV
%=.SIGMA.{ERV(1) . . . ERV(n)}/n or ERV %=.SIGMA.{ERV(1)/x . . .
ERV(n)/z}
[0069] Predictive neural network modeling is a very powerful
predictive modeling technique. It is derived from nerve systems
(e.g., human brains). The heart of the technique is a neural net
(or network for short). A typical network includes layers of nodes
and links between neighboring layers' nodes. The first layer is an
input layer. Nodes of an input layer represent induction fields or
values of nominal induction fields. The last layer is an output
layer. Nodes of the output layer represent prediction values (or
class names), i.e., values of a target field. The rest of layers
are called hidden layers (or middle or internal layers). There is
typically a single hidden layer, but there may be zero or more
hidden layers. For example, the figure shown at the left-hand side
contains a network that determines credit risk levels based on
gender, age and salary. It includes an input layer of 15 nodes, one
hidden layer of 15 nodes and an output layer of 3 nodes.
[0070] As understood in the art of neural networks, each link is
assigned with a different weight. The weights provide for
predictions from the neural network model, as understood in the
art. As shown in FIG. 15, induction fields, in this case customer
related variables and economic related variables, may be presented
to the nodes of input layer. The values are propagated through the
neural network to the output layer. In this process, the input
values are multiplied with weights, summed, and applied to a
non-linear function. The weights are set such that for given
inputs, values of output layer reflect predictions, i.e., large
values (e.g. 0.9) for positive predictions and small values (e.g.,
0.1) for negative predictions. Output values are typically in the
range of 0 and 1. For example, the network of FIG. 15 may output
0.6 for the "certain buyer" output node, 0.2 for the "likely buyer"
output node, which predicts as a probable purchaser, and 0.2 for
the "unlikely buyer" output node, which predicts as a
non-purchaser. It is noted that combined output may not be equal to
1. Neural networks are "trained" to produce certain responses or
predictions by an iterative process, and the weights applied to
each input are adjusted to optimize a desired output.
[0071] Binary categorical input data for neural networks can be
handled by using 0/1 (off/on) inputs, but categorical variables
with multiple classes (for example, marital status or the state in
which a person resides) are awkward to handle. Classifying a result
into multiple categories usually is done by setting arbitrary value
thresholds for discriminating one category from another. It would
be difficult to devise a neural network to classify the location of
residence into the 50 U.S. states. Classification trees, on the
other hand, handle this type of problem naturally. Neural networks,
unfortunately, do not present an model that is readily
understandable as compared to a decision tree, which is easy to
identify initial variables that divide the data into two categories
and then other variables split the resulting child groups.
[0072] While a neural network is one potential predictive model
that may be utilized to predict whether a potential purchaser of a
product who submits an indication of interest, it should be
understood that other predictive and non-predictive logical and
mathematical models as understood in the art may be utilized to
determine whether a potential purchaser who submits an indication
of interest will ultimately purchase a product. For example,
decision trees, stochastic gradient boosting, linear regression and
non-linear regression may be utilized. Each of these models may
enable the various consumer and economic related variables to be
processed in making a determination. The result of the
determination may be a percentage that can be used to determine if
or how many products should be produced for a single potential
purchaser or a group of potential purchasers. For example, if a
potential purchaser submits an indication of interest indicating
that he or she (or a business) is interested in purchasing 100
items of a product and the predictive model predicts, based on
consumer and economic related variables associated with the
potential purchaser, that the likelihood of that potential
purchaser is 25%, then the manufacturer can determine that 25 of
the 100 products should be produced for that person, thereby
producing a more accurate demand or supply to minimizing production
and demand overrun.
[0073] The previous description is of example embodiments for
implementing the principles of the present invention, and the scope
of the invention should not necessarily be limited by this
description. The scope of the present invention is instead defined
by the following claims.
* * * * *